Martyna Kuhlmann – DB2 Regression Tester and Artist.


All tech companies draw international talent, but arguably none more so than IBM. Our Analytics Development team alone has labs in Canada, Germany, India, Japan, China, and the US. It’s fascinating for me to hear first-hand the different paths you took to IBM; we are richer for your talent and for the different ways of thinking and for being you and what bring to the team. From Martyna, I learned what it’s like to live in a tiny village in Poland, steeped in the intimacy of village life and in Poland’s tradition of excellence in mathematics.

You came to IBM as an intern from the University of Saskatchewan. What was it like, moving from the prairies of Canada to the big city?

Leaving my village in Poland was much harder than moving from Saskatoon to Toronto. I grew up in a village of just 500 people, so when I left at 19 years old I was saying goodbye to many people I’d known since I was born: friends and family, but also the people in the shops, the personae of village life. In a small village, people are always helping each other; you know everyone. Now I live in an apartment building in Markham and I don’t know the people living next-door!


What was it that led you to choose computer science as a field of study?

Both my parents had studied math, so that was the family business, you could say, and my plan going into university – that lasted about eight months, by which time I’d had enough of calculus to last a lifetime. I took one computer science class and absolutely fell in love. After three years I interviewed at IBM and I can now admit that I was extremely stressed about it! I hardly slept the night before. I wanted so badly to work for IBM. I got the job, and it was a dream come true. My parents, on the other hand, were supportive but disappointed, in a minor key, not by IBM but by my choice of field: I was the last hope in the family to carry on their math legacy (my sisters, most rebelliously, studied psychology and neuroscience), so when I told my father his comment was, “Well, better computer science than statistics.”

You are practically a black sheep! Why was computer science love at first sight – or first class – for you?

Because it’s not about solving the equation. It’s about creating universal solutions, and I found coming up with innovative ways to solve a problem intensely rewarding. Part of it is the immediate value. I have a problem, I write a program, problem solved. It takes longer to write a big piece of software, but each function is a tangible step towards your goal. In math, you come up with a theorem and it takes months to prove. You could have an idea and start exploring it just to realize a week later that you’ve hit a dead end and you have to start all over again.


The potential for real-world application is also satisfying. My parents’ work might find its applications in 50, 100, or 200 years — that doesn’t mean it’s less valuable, but since I’m a very impatient person I wouldn’t be able to do work with gratification delayed beyond my lifetime. I need to see the impact of my work right away.

What are you working on now?

I work on the infrastructure team, maintaining an environment for testing. Some of the work is fascinating, and some is not especially glamorous, but at IBM you always have a bonus factor: the people are wonderful. That’s what really makes the difference in my work – solving problems with very smart people from all over the world, who also happen to be incredibly nice to be around.

You’ve perhaps found some of the sense of community of your home village — or fostered it, here at IBM. What do you like to do outside of work?

I find myself drawn to the most tedious hobbies imaginable. I picked up painting recently, and I sew … painting was an antidote to being on the computer for hours and hours, a way to rest my eyes and my mind and to be creative using a completely different part of myself. I’ll sit in front of a canvas for hours painting, I find it deeply relaxing. I need to be completely clear here: I have no talent, none. I paint because I love it!

I also have a cat, named ‘Data’ – she has chewed through 23 computer cables in my home. I’m counting, because I actually find it kind of impressive – her focused dedication to cable destruction.


A lot of the people I interview seem to like food – Sebastian Muszytowski, one of our Python experts in Boeblingen, loves to bake, and Phu Truong knows all the best places to eat out in San Jose.

Not me. I eat once a day, at 6 pm. But I love coffee.

What do you see yourself doing in the future? What excites you?

That’s a tough question! I suppose it’s having an impact. Right now, there is a lot of potential on the infrastructure team – we’re planning to create some regression tools and leverage automation, which is all very exciting!  but the minute I notice there are not as many areas to improve, I will look for another role at IBM.

Yes, it’s important that we use our own machine learning technology, so that’s wonderful.

Absolutely, and the potential is so exciting. Sometimes I’m working on code developed years ago, so I can’t just stroll over to the creator and ask about it, but if we can create self-healing technology, imagine the possibilities!



Home town: Księży Las, Poland
Currently working on: DB2 regression infrastructure and tooling
Favorite programming language: Prolog
Top 5 painting inspirations:
1) A busy day at work (art relaxes me)
2) Struggling with a problem (engaging the right hemisphere can work miracles!)
3) Cool characters from games and movies
4) Art on DeviantArt
5) Lack of internet connection
Dinesh Nirmal

Vice President Analytics Development.

Follow me on Twitter @dineshnirmalIBM

IBM Machine Learning for z/OS – Like no other


Like no other Private Cloud

With many of the top banks, retailers, and insurance organizations using IBM® z Systems® , combined with tried and tested virtualization capabilities, EAL5+ security rating and the ability to handle billions of transactions a day[1], the platform becomes attractive as a private cloud for running advanced analytics as well as cloud managed services.

Those organizations are in an enviable position, with volumes of new and historical business-critical data available on such powerful and reliable systems. The sheer volume and velocity of the transactions, the richness of data in each transaction, coupled with data in log files, is a potential gold mine for machine learning applications to exploit cognitive capabilities and do smarter work, more intelligently — and more securely.

Leveraging Machine Learning on z Systems

Set against an asymptotic curve of information growth, Chief Information Officers and data scientists constantly battle to gain deeper insights from the volumes of transactions and log data on the platform (and many other platforms) and turn those insights into concrete gains. In most cases, the CIOs already have astute teams of data scientists and data engineers combing through this data — and yet they see their teams struggle to make enough time for the deep work they’re trained to do.

“Enterprises are well aware of the tremendous potential and value of the transactional and operational data on their z Systems. Yet most of them struggle with how to expose the data within the enterprise in a secure and controlled way thats still flexible enough to foster innovation and support efficient access for a variety of roles data scientists, operations, and application developers. Not an easy task, but organizations that can do so potentially obtain an edge over the competition.”

—Andrei Lurie, DB2 for z/OS Architect, IBM

Machine learning has the potential to be the perfect intelligent app — to hike efficiency, create and cement deep personal relationships with customers, push into new lines of business and new markets while helping to minimize financial risk and fraud.

I have heard customers say that the mainframe has never been hacked. But it doesn’t mean cyber criminals aren’t trying, nor that unscrupulous people aren’t attempting to commit fraud. Having applications that embed predictive models that can analyze, sense, react and become smarter with every transaction and interaction in such a business critical environment brings us a long way toward identifying and preventing potential fraud.

But z Systems is not just about transactions. It is already considered to be a hybrid transaction and analytics processing (HTAP) environment with a complete set of the analytics capabilities and acceleration technologies available today. IBM has also added full exploitation of Apache Spark™ on both z/OS and Linux® on z Systems – a solid base for building, testing and deploying high performance machine learning applications.

“By running advanced Apache Sparkanalytics directly on their production systems, enterprises can improve both the efficiency and timeliness of their insights. Moving Spark inside the mainframe also simplifies and can help reduce security risks as there is only one copy of the data to protect, and that copy resides inside z/OS’s security rich environment.”

— Fred Reiss, Chief Architect, IBM Spark Technology Center

For all these reasons and more, we are delivering the full range of our machine learning capabilities to z/OS essentially bringing advanced ML to the world’s most valued data.

Machine Learning without Compromise.

When asked to describe machine learning I break it down into three perspectives: Freedom, Productivity and Trust. I find these resonate well with customers’ needs.

Freedom. Think of freedom as a set of unified but powerful capabilities such as the flexibility of the interfaces that can be used to interact with machine learning — whether a Jupyter notebook or intuitive graphical interfaces catering to the needs of various personas from beginners to expert data scientists. With support for Python™, Java™, and Scala, different organizations can leverage their preferred programming language and skills when building machine learning applications. Machine learning from IBM can be developed on and deployed across different computing environments such as private cloud and public cloud – including IBM z Systems z/OS with a choice of frameworks such as SparkML, TensorFlow™ and H20.

With the data available to machine learning solutions, users can create advanced algorithms or choose from a set of predefined powerful algorithms and models without requiring advanced data science expertise.

Think of all this capability running on one of the highest performing platforms available: IBM z Systems. It means machine learning can be brought to bear many thousands of times per second[2] — which can help reduce costs and risks, finding and leveraging new opportunities at every transaction and interaction.

Productivity. To make machine learning consumable it has to be easy and intuitive for end users. To this end, IBM machine learning was built around three core principles of simplicity, collaboration (across multiple personas) and convergence of a wide range of technologies from our analytics portfolios and our research laboratories. The user experience is key, whether the user is a data scientist – advanced or beginner — or a computing generalist. Across personas, IBM Machine Learning lets users engage and collaborate on machine learning projects– leveraging the combined skills of the team. Wizards within the tools provide step-by-step processes and workflows that automate many aspects of building, testing and deploying learning machines. As part of the process the IBM Cognitive Assistance for Data Scientists (CADS) automates the selection of the best algorithm given a training data set. It starts by allocating a small part of the data set to each candidate algorithm, then estimates performance on a full data set. It uses the estimate to rank algorithms, and allocates more data to the best ones. It iterates until the best algorithms get all of the data set.

Trust. Once a model is built and tested, it needs to be deployed. A model – in fact the entire machine learning application (learning machine) — is similar to a living organism, evolving and adapting over time as it encounters new data with each transaction and interaction. This is achieved through a continuous feedback loop that enables the model to adapt and change, altering specific parameters within the model itself to become smarter and more consistent over time – while avoiding overfitting.  This auto-tuning is key to reducing manual intervention. Of course some human intervention or model adaptation may be necessary where a human judgement or decision is required. Therefore, keeping track of the version of the models over the lifecycle of the learning machine is important for audit purposes or to fall back to a previous version.  Another aspect of trust is of course the governance and security (the who, how, when, where) of the data, the models, and the many machine learning artifacts. IBM z Systems is recognized in the industry when it comes to security[3]– and a key reason why some of the biggest names and well known organizations across many industries run their business critical applications and data on the platform.

These three perspectives are summarized in the Figure #1 below.


Figure #1 IBM Machine Learning the complete picture.

From a technology point of view, our aim is to free up data science teams to do the deep work that’s being asked of them — work that gets harder and harder as the world moves faster and with less certainty. Ultimately, the gains that CIOs are seeking will come from a collaboration between smart data systems and smart data scientists. Machine learning on z/OS will help enable and encourage that collaboration.

IBM Machine Learning Hub – Beyond the Technology

While the technology aspects may deliver very advanced machine learning capabilities, IBM recognizes the need to nurture and partner with organizations as they embrace and fully exploit its machine learning technologies. The first IBM Machine Learning Hub will provide the means to achieve this, with the aim to accelerate and enrich organizations’ machine earning knowledge and expertise.

The “hub” will allow organizations to access IBM world-class data science professionals who can provide education and training, expert advice on all aspects of machine learning – as well as lead and deliver proofs-of-concept and full client engagements. They focus on delivering tailored machine learning knowledge and skills transfer built around the needs and wants of customers. This combination of both the technology aspects and the knowledge / skills base is an opportunity to provide a unique machine learning experience what I consider to be the machine learning ecosystem.

Let me close this blog post by inviting you to take a look at a short video on machine learning here and reading the recent announcement of IBM Brings Machine Learning to the Private Cloud .


Dinesh Nirmal,

VP Analytics Development

Follow me on twitter @DineshNirmalIBM



[2] based on IBM SPSS Modeler Scoring Adapter for DB2 for z/OS performance

[3] EAL5 + Security Rating

Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation (ASF).

Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.

Python is a registered trademark of the PSF.

Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates.

TENSORFLOW is a trademark of Google Inc.

IBM, the IBM logo, and are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both.

Database and Machine Learning expert, Lead Vocalist on “Dance with Me”


Ketki Purandare: You in the Private Cloud: A bi-weekly series of conversations with IBM talent around the world

The people in IBM Private Cloud come from so many different countries, backgrounds, and traditions that we can sometimes feel like abstractions to each other. These interviews are a chance to correct that feeling — a chance to focus on our common goals and endeavors, and enjoy our diversity.

On a recent trip to Boston, I convinced Ketki Puranare’s manager to free her up from user interface (UI) development long enough to talk with me.

You’ve been a full IBM employee less than a year, but you actually started at IBM in May of 2015 as an intern on IBM InfoSphere information Server (IIS), working in the data governance team. What was that like? I was one of five interns working on a hybrid on-prem solution for data standardization. IBM has a good vision in this area: We could all learn something new, and the product could benefit from the latest technology. We chose AngularJS as the framework for the front end, and for most of the internship, I worked with the Data Standardization team to develop a service to view and add standardization rules.


How did it go? Great! I was able to create a working proof of concept (POC) and did some fun stuff like customizing Apache Tika™ with a csv parser so users could upload a csv with standardization rules. In the second half of the internship, I worked with the Information Analyzer Thin Client which involved writing a REST layer to use existing functionality of the on-prem solution and make it available on a browser as well as developing the entire UI in AngularJS. I learned a lot under the mentorship of our architect— especially since I got to be present from the inception of the project and attend the Design Thinking sessions.

It sounds as though you experienced a true IBM immersion. I realized right away that this is a place where I have a lot of opportunities to grow by working with such experienced people and trying out new challenges with different technologies and tools. There’s so much pooled expertise, it’s inspiring to be a part of it.

Tell me how you decided to study computer science. When did you first know it could be a passion? I wasn’t totally into computers as a child and actually aspired to be a medical student. Then my older brother chose Computer Engineering as his field of study, and I started to get interested. I must’ve been about 14. Sometimes he’d ask me to try out a snippet of code in C/C++ using logic. When I was able to solve it I felt awesome and I think that was what got me interested in pursuing computer science myself.

And it sounds like you’ve been working with logic and algorithms ever since. I know you got your master’s degree in Advanced Databases and Machine Learning. How did you like the program at SUNY Buffalo? The master’s at SUNY Buffalo was a thrilling experience! We had some of the best professors and worked in small groups or even individually on projects like building an indexer and retrieval system similar to Apache Solr™.


Where do you see yourself 10 or 15 years from now? My ambition for the long term is to be a software architect or a Fellow/Distinguished Engineer. I’ve done this at a small scale in my master’s’ projects, but I want to provide blueprints for larger solutions. We have a speed-mentoring initiative here at the Littleton lab which I haven’t taken advantage of yet, but that’s at the top of my list right now.

Would you say you have a philosophy around coding and work? A professor at SUNY Buffalo used to say you never get a project right the first time. Most large projects need to be scrapped and redone to improve the architecture. I had a similar feeling after completing projects in grad school where I’d think I could’ve done it better had I done it another way. You can only get that sense from experience.

I know you spent time in Pune, in the west of India, not far from Mumbai. It’s one of the fastest growing cities in Asia but also really known for research and education. What’s it like? Pune is known as the Oxford of the East! It’s a great city and attracts a large number of students from all over India every year for its quality education. 

Speaking of culture, what do you do outside of work? I used to be the lead vocalist in a band in my school days back in Pune. We used to have concerts and even compose music for local movie productions.


What kind of music did your band play? Fusion music like Indian Classical with Rock. There’s a song called “Dance with Me” that I sang in Marathi as part of a movie called “Mission Possible” in 2010. We were still undergrads back then.


Thank you, Ketki. You’re obviously destined for great things at IBM and I feel so lucky to have you with us on Private Cloud.



Name: Ketki Purandare

Years at IBM: 1

Home town: Pune, India

Currently working on: UI for Information Analyzer

All-time top five songs:

  1. Bring Me to Life — Evanescence
  2. Sweet Child O’ Mine — Guns N’ Roses
  3. Chammak Challo — Akon
  4. New Day has Come — Celine Dion
  5. Vande Mataram — A. R. Rahman


Dinesh Nirmal

Vice President Analytics Development

Follow me on twitter @DineshNirmalIBM


Apache, Apache Soir, Apache Tika, Apache are trademark of the Apache Foundation

Welcome to the Private Cloud


Readers of this blog know that I like to imagine the world through the eyes of my young son. I’m struck by his constant drive to push himself to his next edge of independence. I also know his appetite for danger goes only so far. He understands some of the safety boundaries we have set to protect him from the chaos and to help him thrive. Home is a safe environment in which we prepare him for the outside world, public places like school where he interacts with other students, sharing resources with others.

His effort to find the right balance of exploration and safety resonates with what we mean by “private cloud” and preparing clients for a hybrid cloud environment: private plus public, as in figure #1 below:


Figure #1: Hybrid cloud the path toward optimal business outcomes

Hybrid cloud can provide ultimate flexibility by allowing organizations to place data and associated workloads where it best makes sense – for optimal business outcomes which I discuss in more detail later in the blog.

Private Cloud defined

In the simplest terms, private cloud (sometimes also called internal cloud, dedicated cloud or corporate cloud) provide all the benefits of cloud provisioning, management capabilities along side the scalability, agility, and the developer-driven services available from cloud vendors — but behind the firewall. Figure #2 below offers some details about the differences.

Public and private clouds are both destinations for the execution of business workloads. More and more, we see organizations eager to take a hybrid approach which allows those workloads to seamlessly execute “together” across public and private cloud allowing those customers ultimate flexibility based on (but not limited to) :

  • The volumes and types of data
  • Sensitivity of the data
  • Performance and service levels required
  • Security requirements
  • Business criticality
  • Data regulation and governance
  • Types of systems, processes, and applications

How you put the pieces together depends on the needs of your business. There are many economic and service level factors to consider. A private cloud is often the responsibility of the organization running it. Besides the factors mentioned above, the responsibilities can include: hardware, software, support, maintenance, service-level agreements with the business and all the necessary human and technical resources associated with it. With a public cloud many of these economic and service level responsibilities can often be devolved to a third party – allowing the organization using the public cloud to focus on its core business processes and needs.

That said, some enterprise customers are seeing that many of the benefits typically associated with the public cloud — lower cost, speed of provisioning, reduced management — are increasingly available on private cloud configurations that also allow data to be governed securely, smoothly, and transparently.


Figure #2 : Private and Public cloud differentiation

Life behind the firewall

What we mean by “behind the firewall” depends on individual clients and their needs. It might mean that the data is maintained completely within a client’s own protected data center by the client themselves. Or, that the data and apps live on fully dedicated bare-metal servers off-site, supported by a cloud vendor like IBM managing hardware, maintenance, connectivity, redundancy, and security on the client’s behalf, all of which help that client drastically reduce capital expenses for the servers, in-house IT staff and the burdens of obtaining and updating software.

Avoiding expenses and hassle is just the beginning of what’s possible, but let’s first consider why maintaining a private cloud while exploring public cloud options is the right fit for so many of them. Broadly, private cloud configurations can address two particular needs:

  1. The need to create a highly secure and reliable home for sensitive data, to perform advanced analytics, and to maintain data sovereignty — while allowing that data to be in conversation with data and analytics that are accumulating in the public cloud. In this sense, private cloud is one end of a private/public cloud hybrid configuration in which data is accessed, moved, and managed using secure, service-layer APIs.
  1. The need to modernize systems and processes — even behind the firewall. Organizations who see the benefits of maintaining a private cloud nevertheless demand the clear advantages of public cloud I mentioned before: elastic scalability, agility, consumability of API-driven services, easier management, and rapid provisioning, to name just a few. The key concepts here are:
  • virtualization — The use of virtual operating systems and highly elastic virtual processing power.
  • federation — The ability to take several different physical entities and represent them as a single logical entity.
  • data fabric — A software-defined approach for connecting disparate storage and management resources across private and public cloud. The approach enables multiple components to interoperate through a set of common, standardized services and APIs regardless of physical location, type of data, or type of service. As mentioned above, clear data governance is particularly crucial in hybrid environments — and even more so when country-specific compliance rules require different data policies across geographies.

As my colleague wrote:

Private Cloud is about delivering an elastic data fabric behind the clients firewall. From a user perspective, the experience goes from “Provision me a database to do xyz” to “Here is my data and analytical needs, please help.” There is no need for dedicated repositories for a specific application and user needs are met automatically, with limited human intervention.


Figure #3 : Hybrid cloud architecture  

Path to Cloud Benefits

Regardless of their focus, organizations are hungry for simplicity, transparency, and the ability to move toward cloud without starting from scratch. They know that their future success lives at the edges of wide networks, at the points of direct contact with customers and the outside world. Mobile phones, IoT sensors, and other connected devices are the new lifelines to current and potential customers, who passively or actively exchange volumes of data with servers. That data runs the gamut in terms of privacy and sensitivity: from the temperature of the toaster to credit card information, from glucose levels to the current whereabouts of my son’s backpack. All that activity at the outer edges of the network has shifted a portion of the business into the cloud even for traditionally cloud-wary sectors like finance, government, and healthcare. For those organizations, a private cloud offers an environment for core-mission, transactional workloads even as the public cloud allows them to explore CPU-intensive or streaming applications that are (for now) less central to the business. Not surprisingly, these sectors are exploring tunable hybrid cloud infrastructures. Figure #3 above offers some perspective.

Alongside the need to stay connected to customers, pressure to come to the cloud is also intense in terms of cost savings, easier management / provisioning, and — perhaps ironically — security. Security threats evolve so rapidly and attacks come from so many directions that internal security teams can struggle to keep up. And since some of the most severe cyber-attacks can come from within a company’s own ranks rather than from exterior bots or hackers, the internal teams are finding that the security of the cloud providers can be advantageous in terms of speed, currency, and completeness. As Cameron McKenzie points out, “Enterprises are starting to seriously consider the cloud as a viable option because they’ve realized that security is a battle they can’t win on their own.”

Advantages of IBM Private Cloud

Right now, IBM Private Cloud can help provide the best of the public and private cloud worlds. In fact, a recent InformationWeek post about private cloud states that “IBM is the market leader.” Our deep, in-house knowledge can help organizations breathe easy in terms of performance, cost, security, and white-glove attention and support. We start with the assumption that those organizations need to leverage the systems and processes they have in place by cloud-enabling their investments — rather than starting from square one.

Think of the IBM Private Cloud as a stack. You still need that physical infrastructure that offers high availability, scalability, performance — a strong data and analytics foundation to ingest, prepare, wrangle, discover and transform data into trusted assets. On top of that you need the ability to manage existing investments in applications and solutions as well as creating new services and apps that are cloud-enabled and can be rapidly provisioned – everything from management of the infrastructure to a collaborative development environment. Oh, and the need for security and governance of the data, transactions and applications over their lifecycles doesn’t go away.  All these layers in the stack (regardless of whether an organization buys into all of them) can be provided by IBM today – and many of them were well established and available before the mainstream adoption of cloud.

Customer environments without exception are multi-vendor, consisting of an array of heterogeneous platforms. That’s why the private cloud platform is designed to co-exist and integrate with many different technology infrastructures. The goal is to bring cognitive analytics capabilities to wherever the data is with flexibility in mind – such as delivering offerings in multiple form factors to help meet the diverse needs of our clients on their cloud and cognitive journeys. A great example is the use of Docker images that make it possible to run our analytics and other offerings across many different infrastructures leveraging the attributes of private cloud.

Innovation and Investment for client success

We’re innovating and investing on clients’ behalf to help bring them not only the expected benefits of the private and public cloud, but with the robust internal partnerships with IBM Power and IBM z Systems, business partners like the ones described above, and access to market-leading data management solutions, world class descriptive, predictive and prescriptive analytics solutions – all in a cloud-enabled integrated, secure and governed environment. All this comes together within the private cloud data platform with tried and tested infrastructure, governance, security, data fabric capabilities and cognitive computing services – with the flexibility to provision data and policies across private and public cloud environments. This is an optimal hybrid model.


In subsequent posts, we’ll look at private cloud strategies related to data repositories, analytics, content management, and integration/governance issues — and how these strategies braid together.

In the meantime, I encourage you to click the IBM private cloud page – a great place to explore and try some of the capabilities that exist today, and get a preview of what’s coming soon.


Dinesh Nirmal, Vice President, Analytics Development.

Follow me on Twitter @DineshNirmalIBM

Phu Truong: Humble Leader, Loves Logic, Hates Calculations


You in the Private Cloud: A bi-weekly series of conversations with IBM talent around the world

If you’ve seen the movie “Hidden Figures” — and if you haven’t I highly recommend you do, and not just because IBM is a central character — you’ve seen how the race to get a man into space was profoundly affected at the 11th hour by one courageous woman, and the help her boss, her friends, her teachers, and her family gave her to get to that one minute in time when she made a difference.

People first. To build rockets to the stars and machines that think, people need to dream things up, and work with sustained, supported effort to make them real.

We have many talented people in IBM Private Cloud. This year, I’ll continue to meet and talk to as many of you as I can and I’ll post our conversations here every two weeks. My hope is we’ll get to know each other, and feel even more connected and supported in our work.

This week, I was able to lure Phu Truong away from coding on the IBM data platform to meet at IBM Silicon Valley Laboratories, San Jose, CA.

You were an intern with us until just a few months ago. Why did you choose to come to IBM full time, out of all the choices in Silicon Valley? The appeal of IBM is the opportunity to work on new technologies, specifically, new technologies on the back end. A few weeks into my internship the senior engineer I worked with set me to work on learning Node.js® and React. I want to be a full stack engineer so now I’m working on UI, but to be really great there you need a feel for art, and I don’t have that. The back end is pure logic. I loved it, so much so that I started staying very late at night to work.


Some people love their jobs because of people, or culture, but clearly, you love the technical work. How did you decide on computer programming as a profession? I come from Vietnam, and I had no programming background there, to be honest. I studied mathematics at university and planned to go into it professionally, but I’m very bad at calculations. I make mistakes all the time! What I love about mathematics is logic — the feeling I get when I solve a problem using logical thinking is intensely satisfying to me. I feel very good about myself.  So when I came to the U.S., I had a fresh start. I asked my friends to help me find a field that uses logical thinking to solve problems, and they recommended computer science. One week into my first CS class, Data Structures and Algorithms, I knew I’d found my profession.

So now you’re at IBM, you worked on the Data Science Experience (DSX) and now you’re working on the IBM data platform. Are you thinking of following the full path from engineer to Senior Technical Staff Member (STSM) to Distinguished Engineer (DE) to IBM Fellow? I don’t know, that may be too much!


I hear great things about you so maybe not! You’re already mentoring others in your team on Node.js, after being here only a few months. I consider it more like sharing knowledge. When a colleague comes to me with a question, I might know something they don’t and they might know something I don’t. I might say something wrong when we’re working together and that’s an opportunity for them to correct me and for me to learn. Growing up, I helped my younger brother with his schoolwork, so I guess it’s natural for me to help. But it benefits everyone.

What do you like to do outside of work? I like to play Ping Pong with my friends from San Jose State, or go with them to the beach. And I love to travel—I want to go to Cancun, because of all the natural landscapes the beach is my favorite and I’ve heard it’s spectacular there. After that, Paris and London. I love eating out, so much so that I tell my friends I want to marry a chef!

You have an adventurous spirit! IBM is an international company so, I don’t know about Cancun, but travel to Europe is likely. What’s it like living in the heart of Silicon Valley after growing up in Vietnam? I grew up in Saigon, in a very tall, very thin town house: Saigon is famous for thin houses. Here, being surrounded by rolling green hills and close to the beach is wonderful. I think my family worried about me when I moved here, not that it was dangerous, but that I might just chase money and give up on my education: I worked as a waiter, a data entry clerk and a school bus driver, any job I could get, I took. But I never gave up on my education. I think now they don’t worry about me anymore. I think they might be proud of me.


You’ve achieved a great deal here in a very short period of time, making a significant contribution to two products that customers like. It’s tremendous, and I’m happy you’re here. I am as well. I think the biggest difference between Vietnam and here is in education and learning. In Vietnam, education was driven by memorizing things and was not interesting to me. And, we are taught to do exactly what teachers tell us to do; they don’t give students a chance to explore their interests. So to be first at San Jose State and now at IBM where it’s part of my job to learn new skills—well, I like it very much.

Name: Phu Truong
Hometown: Saigon, Vietnam
Currently working on: IBM Data Platform
Favorite Programming Language: Node.js
Top 3 travel destinations: Cancun, Paris, London
Best Vietnamese Food in Silicon Valley: Pho Y 1 on the Capitol Expressway, San Jose

Dinesh Nirmal,  

Vice President, Analytics Development
Follow me on Twitter @DineshNirmalIBM

Node.js is a trademark of Joyent, Inc. and is used with its permission. We are not endorsed by or affiliated with Joyent.

Talking with IBM Talent around the World


Introducing Kewei Wei, from Beijing China

My founding principle for our organization is, “people first.” I believe that to build great products, you start with talented people and invest in their work and their individual well-being. The great products they make will in turn bring customers.

One of the best things we get from work is the satisfaction of being part of a group—bringing a divers set of skills together to accomplish something that no individual can do alone. We’re wired to feel good when we act together towards a common goal—Sebastian Junger (author of “The Perfect Storm”) writes in “Tribe” that interdependence and community are required for human happiness. If you played in your school orchestra or soccer team, you know the feeling; I get it with my running buddies when we silently sprint the last half mile, pacing each other to the end of the trail.

In the IBM Private Cloud team we have almost 3,000 people, and a list of clients who represent  a significant piece of the financial, government, and commercial world. And it just so happens that we sit at a “tipping point” with customers wanting the advantages of all that cloud offers, plus premiere machine learning technology, in a private cloud environment.

As a team of thousands working in labs from Austin to Zurich we have grown to know each other beyond the sometimes less than energizing interface of email.

I decided to conduct a series of interviews, You in the Private Cloud: look for them weekly, here. It’s a great way to bring new information forward that increases our collective knowledge.

People who are talented and engaged tend to have interesting lives outside of work—hobbies that test limits and relationships that matter, so I’ll be asking about that as well.

To kick off the series I talked to Kewei Wei last week in Beijing. Kewei is the lead developer for Machine Learning on z Systems in our IBM China Development Lab.


Kewei and I had just come from a meeting with a customer – one of the largest financial institutions in the world. They had just told us they want to be part of the closed beta of our machine learning offering on  z Systems – a testament to the tremendous work Kewei and the team have done. We sat down in a quiet part of the office and I asked him what he thought:

Kewei: This is exciting news! We weren’t sure how quickly this customer would move into new technologies.  But I believe they were, to a certain extent, feigning reluctance; they’ve been trying all along to find a balance between moving ahead with machine learning and other new technologies, and keeping the stability and security that they absolutely need to maintain. They cannot take risks with financial information, given their global position and their scale. Now we have an opportunity: if we can show them during the closed beta that we have the ability to be both of these things to them—a protector of security and stability, as we always have been, and now also the conduit to state of the art machine learning technology, they’ll take the risk and move forward with us.

You worked on z Systems for 10 years. Then, 3 months ago, out of the blue you’re told you’re leading Machine Learning on z Systems , and you have 90 days to deliver. How was that work day for you?
To be completely honest, my first thought was, “This is impossible”. In ten years in the z world, we’d never done anything like this; our release schedule was more like 3 years. But we did it, and I believe it’s because we were all passionate about machine learning. We know it’s the technology that’s going to make the greatest change for the world, and that’s something we all wanted to be part of. So we put our heart into it. We learned the open source libraries, the skills we needed to deliver the new micro-services structure, and, critically, we learned how to prioritize. With so much to learn, if we hadn’t done that, we wouldn’t have been able to deliver.

Why is machine learning so exciting to you? What would you like to solve with AI?
I’d like to build machine learning AI to help us perform systems facediagnoses and fix defects. That way, we humans could have time to focus on how to make our world better, instead of just making things work correctly.


A fine distinction. Tell me your take on the Private Cloud.
I think it’s a good thing. We’ve been hearing about the importance of public cloud for a long time. Customers  are telling us they will not be ready for the move to public cloud in a short period of time. But they’re also telling us they can’t afford to wait years for security to mature in the cloud. They need machine learning now, behind the firewall, to be competitive.

Private Cloud represents the reality of the market—what our clients are asking for. It means we’re helping them move forward, but without having to face some of the perceived risks of some public clouds.

I also believe innovation in machine learning will happen in Private Cloud. The foundation of machine learning is, of course, data, and as of today, the majority of critical data is still in the hands of customers who keep it behind the firewall. And customers know they have to use machine learning in their core business, which is behind the firewall. They’ll invest in private AI, and we’ll get to build that.

In terms of innovation, what do you see in China that might come to the US from China’s hyper-evolved app technology, or from the broad consumer adoption of virtual reality there?
Honestly, looking at from technology perspective, there is nothing new in what China is doing today. I think the key is to move fast. Don’t wait, listen to consumers, be willing to take risks, dare to fail and always move faster than your competitors

Where did you grow up, and how did you come to be an engineer in Beijing?
I’m from Jinzhou—it’s a city of about 3 million people (small, in Chinese terms) in Liaoning Province in the northeast of China. My father was a bus driver and my mother worked for an oil company. I am very proud of them! When I was 18 I came to Beijing and enrolled in University. In one of my very first classes, a professor helped me write a Pascal program to simulate a game of chess. I was shocked that this was possible—and in a way I never stopped being shocked, excited, I mean, by what we can do with programming.

What do you like to do when you’re not programming?
Read! I love history books. I just finished “Zhang Juzheng”, I a biography of a famous Chengxiang –a prime minister- in the Ming Dynasty. I’m looking for a book on European history for my next one. I bought Gibbon’s “The History of the Decline and Fall of the Roman Empire” but a few chapters in I realized I’d better start looking for a thinner version of it.

Realistic. I also love to travel with my wife and my 5-year-old son.  I think the most fun thing in life is being in a beautiful place, in the mountains or in a relaxing beach city like Xiamen, with the people you love most.


What do people talk about in China when they talk technology? Are people concerned about security and privacy? Or Artificial intelligence displacing the traditional work of people in the labor market?
What concerns individual consumers far more than security in China is convenience and lower cost. For example, the latest hot business here is food delivery, people love it that we can order a food from a nice restaurant from an app and get it within 30 minutes, for less than we’d pay to eat in the restaurant. Part of the reason it’s so low cost right now is because China’s internet companies are investing without regard to cost: they want to lock in users for long-term return.

It’s almost the inverse of the concerns of government or large enterprise: individuals choose convenience over security every time. For example, you can pay by Alipay or Wechat almost everywhere, including places like vegetable markets where POS probably will never exist. Alipay and Wechat are not as secure as credit cards, but they are far simpler to use. It helps that Alipay and Wechat have committed to compensate consumers if you lose money because of any security defects.

As for AI displacing traditional labor, people in China don’t worry about it much. AI is still a pretty new concept to Chinese. What we know is just AlphaGo or Waston. It still sounds like something far off in the future, not close to our daily lives at all. But AI is starting to draw more and more attention, so this could change soon.

Overall, tech in China is booming, and companies like Alibaba and Huawei are knocking at your door. Why stay at IBM, with those kind of opportunities?
Oh, that’s an easy one: the people. In my opinions you can’t find a better company in the world for talent. I have the greatest people around me, and it’s because of them I had the confidence that I could deliver this project on time.

Kewei, having you on the team has been a blessing: you have been a true IBMer, and delivered machine learning on z Systems in such a short time , an impossible task. Companies would probably not buy our products without people like you. Thank you.

You are welcome. I enjoy it. If I worked somewhere else, I’d have to quit and look for a new job every time I wanted to try something new —but not here. AtIBM, we have tremendous chances to try new things we like to do.

Name: Kewei We
Years at IBM: 11
Lives and works in: Beijing, China
Currently working on: Machine Learning Z Systems
Favorite programming language: PL/X
His top 3 beach books for not-so-light reading: “Three Kingdoms” by Luo Guanzhong, “Blood Remuneration Law” by Li Qiang, and “The Shortest History of Europe” by John Hirst

Dinesh Nirmal,  

Vice President, Analytics Development
Follow me on Twitter @DineshNirmalIBM

Should’ve, Could’ve, Would’ve – Making the Optimal Decision.


In a previous blog I talked about the value of machine learning and how it could help organizations by making smarter predictions by continually learning and adapting models as it consumed new interactions, transaction and data.  I compared that to how my son embraced learning about the world around him to become gradually smarter, more knowledgeable.  But that doesn’t always mean he is going to make the best decision because he may not have all the information or foresee or correlate past events.

So, I guess there are times when we wonder – or get asked by others – whether the decision we just made was the best possible one.  Think of when you made your last hi-tech or car purchase.  It’s often difficult to judge at the time if it was the best choice as there are many parameters involved including (but not limited to) logic, price, best value, best meets our needs and wants, emotion, political.  How many times have you sought to justify that purchase immediately after by doing even more research on reviews by others?  Don’t worry. It’s a natural human emotion known as post purchase cognitive dissonance.

The collective name for this capability is called prescriptive analytics and it relates to predictive analytics as shown in figure 1 below.


Figure 1: Descriptive, predictive and prescriptive analytics relationship.

Making that optimal decision at a business or boardroom level becomes even more exacerbating and complex, involving many people and large sums of money. Every decision needs to balance risk with cost and with benefit.  Quite often there are conflicts of interest, bias, power struggles involving political and emotional agendas that can result in suboptimal decisions being made for the business.  Oh the frailties and flaws of humankind!   The crux of the matter is there are far too many parameters, data points, correlations and patterns for us humans to be aware of to make the optimal decision for every transaction and interaction.  So there are times we need these capabilities to augment and balance our own judgements.  These decisions can range from a simple purchase or where best to distribute warehouse stock, where to place emergency services in the event of a pending disaster or be financial focused or risk based through to decisions that ultimately help preserve or save lives in the medical field.

Decision Optimization at every transaction.

IBM has been providing decision optimization for many years as part of the business logic within applications with products such as IBM Decision Optimization and its market leading CPLEX Optimizer engines.  Together these offerings are providing a collaborative environment for key personas to deliver a powerful solution application to line of business users to make better and faster decisions in planning, scheduling and resource assignment. In short, the aim of Decision Optimization is to remove the guess work from making a decision by “prescribing” and automating the best decision for you. Just to put your mind at rest – the business user / planner still has the ability to change that decision, interact with it – it is something which is recommended, but the end-user can still own that choice of whether the decision will be operationalized or not.

There are three key personas that are involved in building a decision optimization solution as part of the optimization application development cycle (figure #2) namely the business analyst, operation research (OR) expert, application developer – and the LOB consumer of course.


Figure 2: Optimization Application Development Cycle.

Together these personas have a broad range of responsibilities and needs such as:

  • Create optimization models / algorithms.
  • Use APIs to embed in decision making applications.
  • Solve models with the IBM CPLEX Optimizers.
  • Analyze results.
  • Use Rest APIs to embed in decision making applications
  • A Collaborative development environment to build & deploy enterprise-scale optimization based applications
  • Visualize trade-offs across multiple plans, scenarios, KPIs.
  • Optimize costs vs robustness.
  • Recommend decisions hedged against data uncertainty.

You don’t have to be an Operations Research Expert to use IBM Decision Optimization.

The goal is for decision optimization to be consumable making this powerful technology available to decision makers who are not necessarily trained in mathematical modeling.  Business managers need to be able to use their own business language to define a decision model.  To support this, the IBM Decision Optimization R&D team created the proof-of-concept for “Cognitive Optimization”.  The goal of Cognitive Optimization is to allow business users to directly create and work with Decision Optimization models, without requiring the intervention of a mathematical whiz.    It uses a combination of the business data and the business user input, in natural language, to figure the business intent, to suggest potential decision models, and then to use those models for “what-if” and trade-off analysis.”

Similar with what was announced for the Watson Machine Learning service, Optimization as-a-Service will be also available as part of the data science experience (DSX), in addition to being a standalone cloud, hybrid, and on-premise offering, for the personas mentioned above to experience decision optimization as part of a prescriptive analytics solution as a stand-alone service.   If you consider the high level flow of optimization as-a-service it looks very similar to machine learning as-a-service as shown in figure #3 and, as such, they share many commonalities which makes DSX an ideal collaborative environment for machine learning and operations research practitioners.


Figure 3: Optimization-as-a-Service high level workflow

Clear business benefits reducing time, risks and costs.

In summary the combination of IBM Watson Machine Learning and the Optimization as-a-service capability can help organizations across every industry make progressively smarter cognitive insights and act on optimized decisions while removing the human frailties of emotional, political and personal bias.

Organizations are already experiencing the benefits. For example, a global tire manufacturer uses IBM decision optimization solutions to help:

  • Optimize long-term production planning, considering up to 10 million constraints across all products and plants
  • Predict when major machine bottlenecks are likely to occur, enabling staff to take corrective action early on
  • Drive smarter decision-making with 30 times more what-if scenarios evaluated
  • Save up to 30% of planners’ time, allowing them to focus on value-add initiatives

Your Optimal Decision

Over the next few months this is going to be an exciting space to watch as decision optimization moves forward with three key design principals of simplicity, collaboration and convergence of the technologies helping more organizations complete their cognitive journey. So what do YOU do next? What’s YOUR next best decision?  Sign up for Optimization as-a-service as part of the Data Science Experience here.

Dinesh Nirmal,  

Vice President, Analytics Development

Follow me on Twitter @DineshNirmalIBM

Business Differentiation through Machine Learning.


I look at my child and marvel as he embraces the ever fast moving world around him, adapting to new experiences, grasping technology, absorbing a bombardment of information from so many sources.  It’s staggering to watch their progress from basic learning of just accepting facts they are taught, to augmenting those facts with their own knowledge, to asking questions, using their knowledge to express their opinions and values to others, then challenging facts and hypotheses that they once accepted to adapting their knowledge, understanding, decisions and value systems. And then they start reasoning with their parents! Their brains are like sponges using their senses of seeing, hearing, touch, smell and taste, absorbing information, experiences, emotions, constantly connecting new events and situations with those from the past – pushing boundaries and testing us.

Watching my son learn was fascinating but quite frankly businesses can’t wait years to learn about their data and how best to act on it.


From Business Intelligence to the Intelligent Business.

The business world is complex with many fast moving dynamics.  We only see the tip of the iceberg when it comes to the total information that exists at any point in time as it grows exponentially. Nor are we able to fully harness the collective thoughts or full knowledge of the people around us.  This is where cognitive technologies such as the many forms of machine learning (ML) can really be a huge advantage to business people.

Simply put machine learning is the capability of computers to learn without being explicitly programmed.

Imagine systems that have total information awareness. “Nodes” that connect with each other on premise and in the cloud that learn about each other like synapses in the brain.  Harnessing the collective consciousness of these “machines” (don’t restrict your thoughts to hardware here) and applying intelligence to advise on the “optimal” decisions resulting in the “optimal” outcome is what every organization would want. I think I just defined the killer-app for business.

What does it look like?  Well I envisage it as the graphic below – a cycle or workflow of constant learning.

Screen Shot 2016-06-29 at 8.34.45 AM

Figure 1 : Machine Learning  – a cycle of constant learning


What’s out there today?

The good news is that much of these capabilities exist in IBM solutions today and much more in our research labs yet to emerge and potentially change the industry.

So how do you get started?  There are four main states when considering knowledge: Know what you know, Know what you don’t know, Don’t know what you know, Don’t know what you don’t know.  And all this starts with your data. We have solutions that can catalogue what information you have, discover information that you didn’t know you had and help identify information that is missing or untrustworthy.  These can help reduce not knowing what you don’t have.

Next you need capabilities that can ingest information where ever it is without having to move it.  From this data we can apply machine learning (ML) that can discover relationships, rationalize, predict what might happen next and actually become intelligent (all knowing) about the data and all previous outcomes.  This can only be achieved through continuous learning and adapting. There are also other steps around optimization of that learning that can be read here. There are many vendors that claim to provide one form of ML capability or another and while that may be interesting in its own right, on its own it has minimal value.  Combining all forms or learning and analytics can take you closer to the bigger picture of “cognitive” in which IBM strives to be a leader. I’ll expand a little more on cognitive later in this blog.


From Tic-Tac-Toe , Jeopardy, Crime Prevention, Cancer Treatments and more.

In my early student days I learned how to code simple recursive algorithms using rote learning to produce a game of Tic-Tac-Toe that could not be beaten after five or six games. It did this by minimizing its losses by avoiding outcomes that would lead to failure.  In 2011 “Watson” competed on Jeopardy and beat the top contestants.  It had access to 200 million pages of structured and unstructured content consuming terabytes of disk storage including the full text of Wikipedia but was not connected to the Internet during the game. For each clue, Watson’s three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game’s signaling device, but had trouble in a few categories, notably those having short clues containing only a few words – kind of similar to human language ambiguity.  Its natural language processing, predictive scoring and models were key to its success.

In February 2013, IBM, Wellpoint and Memorial Sloan-Kettering Cancer Center announced the first commercially developed Watson based cognitive computing technology to be implemented for utilization management recommendations to physicians in lung cancer treatment at Memorial Sloan Kettering Cancer Center in conjunction with health insurance company WellPoint Inc. (now Anthem Inc). At the time of writing this blog, nearly 43,000 organizations have registered to use IBM Watson Healthcare Analytics Platform .(1)


Machine Learning 101

I am often asked “How confident are you in that decision?” I used to base my answer on the strength of the information I had – and some of it was intuition, gut feel, life experiences.  But now I can scientifically put a figure on it – as precise as the underlying information allows of course.  In fact, well established IBM analytics products have been using these predictive models and scoring algorithms (included in Watson Analytics) across many industries for years to better manage risk, and identify potential fraud (I’ll tell you about my credit card experience in a later blog while attempting to rent a vehicle).  In 2015 IBM donated its SystemML to the Apache™ Software Foundation. SystemML is a flexible machine learning system designed to auto-scale to Spark and Hadoop® clusters and extends the core machine learning in the Apache Spark™MLlib libraries. We also have machine learning in many other forms available as-a-service including but not limited to Natural Language Classifier, Retrieve and Rank, AlchemyVision –and many others that we will describe in more detail later.   Below is a diagram of how machine learning is implemented as a generic model.

Screen Shot 2016-06-29 at 8.34.04 AM

Figure 2 – A generic machine learning model


Beyond Traditional ML – Learning through Senses.

As mentioned earlier IBM has many other forms of machine learning technologies that help  differentiate our cognitive capabilities from other vendors. Using vision, language and other ML technologies begins to more closely simulate human behavior of understanding, reasoning and learning.  Below are some of the key ML technologies that are being used by many organizations around the world.

AlchemyVision is an API that can analyze an image and return the objects, people, and text found within the image. AlchemyVision can enhance the way businesses make decisions by integrating image cognition. Organizations across a variety of industries ranging from publishing and advertising to eCommerce and enterprise search can effectively integrate images as part of big data analytics being used to make critical business decisions by better targeting ads, organizing image libraries, improve consumer experience, monitor your brand, profile target markets, improve researching.  The Tabelog case study is particularly interesting. Over 40,000 foodies visit the Tabelog site, confident that Tabelog will provide accurate and reliable recommendations. Additionally, more than 200,000 registered restaurants use the site to help brand and promote their establishment. Try it now

Natural Language Classifier is a service that enables developers without a background in machine learning or statistical algorithms to create natural language interfaces for their applications. The service interprets the intent behind text and returns a corresponding classification with associated confidence levels. The return value can then be used to trigger a corresponding action, such as redirecting the request or answering a question. The Natural Language Classifier is tuned and tailored to short text (1000 characters or less) and can be trained to function in any domain or application. Typical usage scenarios are:

  • Tackle common questions from your users that are typically handled by a live agent.
  • Classify SMS texts as personal, work, or promotional
  • Classify tweets into a set of classes, such as events, news, or opinions.
  • Based on the response from the service, an application can control the outcome to the user. For example, you can start another application, respond with an answer, begin a dialog, or any number of other possible outcomes.

You can try it out by clicking here.

Retrieve and Rank is a service helping users find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data. Built on top of Apache Solr, developers load their data into the service, train a machine learning model based on known relevant results, then leverage this model to provide improved results to their end users based on their question or query. The Retrieve and Rank Service can be applied to a number of information retrieval scenarios. For example, an experienced technician who is going onsite and requires help troubleshooting a problem, or a contact center agent who needs assistance in dealing with an incoming customer issue, or a project manager finding domain experts from a professional services organization to build out a project team.  You can try it out here.


From Crystal Ball Predictions to Prescriptive Actions.

So predicting a hurricane or an outcome or recognizing images and video or understanding the importance of a particular piece of information is just a first step. Now you need to take prescriptive actions on that understanding to, for example, survive the hurricane or achieve business advantage from a business opportunity that may only last a short time.

IBM has the capabilities not only to help provide an advanced and wide range of machine learning capabilities and to act on them – but to do it in the “right-time” – for example being able to predict whether a trade or bank transaction is legitimate or fraudulent 15,000 times a second.  In business, speed is of the essence.


Cognitive + Cloud = Optimal Business Outcomes

So it seems that machine learning can be advantageous (even smarter) and faster than humans in certain situations – given complete and accurate data. Combining the wide range of advanced machine learning capabilities described above with the ability to act prescriptively on what has been learned with IBM’s full cognitive analytics capabilities can yield many opportunities to potentially out-maneuver your competition, act with confidence and help your organization become the optimal Intelligent Business. Cloud is key here because it has the potential for everyone and everything  (including data) to be interconnected.

Machine learning can help enable cognitive systems to learn, reason and engage with us in a more natural and personalized way. These systems will get smarter and more customized through interactions with data, devices and people. They will help us take on what may have been seen as unsolvable problems by using all the information that surrounds us and bringing the right insight or suggestion to our fingertips right when it’s most needed. Over the next five years, machine learning applications could lead to new breakthroughs to help amplify human abilities, assist us in making good choices, look out for us and help us navigate our world in powerful new ways.

In summary machine learning in all its forms has the potential to bring the collective knowledge and consciousness of humans and machines together to help make the world a better, safe place.

In the following months the team and I will be taking you on a journey exploring many more aspects of IBM’s cognitive and learning capabilities.

Of course you won’t be able to predict where I’m taking you next.   🙂

For more information on IBM’s cognitive and machine learning capabilities click this link


Dinesh Nirmal,

Vice President, Development, Next Generation Platforms, Big Data & Analytics

Follow me on Twitter @DineshNirmalIBM


Jean Francois Puget (PhD),

Distinguished Engineer, Chief Architect IBM Analytics Solutions

Follow me on Twitter @JFPuget


Foot notes

(1) IBM Watson Analytics Team based on number of registrations as at June 27 2016

TRADEMARK DISCLAIMER: Apache, Apache Hadoop, Hadoop, Apache Spark, Spark are trademarks of The Apache Software Foundation.



Why a Cloud First Strategy Can Benefit Customers with IBM BigInsights on Cloud.

Hadoop – the early years

The origins of Apache™Hadoop® go as far back as 2003 in reference to the emergence of a new file system, followed by the introduction of MapReduce and the birth of Hadoop in 2006. It achieved notoriety and fame as the fastest system to sort a terabyte of data and when it became an Apache open source project (Apache Hadoop) it sent a signal that it was ready for prime time.  The world never looked back.  Within IT shops and even board rooms there was huge interest, excitement – even hype with suggestions that it might replace the enterprise warehouse.

A quick Hadoop refresh

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Maturity, BigInsights platforms and the move to cloud

All technologies move through a maturity cycle. Hadoop is no exception and is maturing fast. IBM® saw the opportunity to build enterprise ready mission critical Hadoop based solutions delivering its BigInsights™ portfolio which adds significant value to the core Apache Hadoop open source software. IBM helped lead and drive the Open Data Platform initiative (ODPi) [see] to encourage interoperability across different Hadoop vendors.

Built around a no-charge open source based core which includes Apache Spark™ called the IBM Open Platform with Apache Hadoop (IOP), IBM BigInsights brings a rich set of capabilities from advanced and high performance analytics such as BigSQL, to visualization through BigSheets all neatly brought together to meet the needs of different personas.  IBM BigInsights is cited as being a leader in The Forrester Wave™: Big Data Hadoop Distributions, Q1 2016 available from Forrester.

Usage of Hadoop in general varied widely across customers – some having multiple thousand node clusters and others just 5 or 10. Having 100s or thousands of nodes might be off-putting for some customers in terms of capital and management costs.  With BigInisghts having established itself as a leader and with IBM focused on a Cloud First Strategy, we saw the opportunity to help customers reduce these capital and management costs, to enable them to focus on running the analytics for business advantage while providing BigInsights on a dynamic elastic and scale out infrastructure in the cloud through IBM SoftLayer and Bluemix technologies from any of our many data centers around the world.

Screen Shot 2016-05-16 at 2.10.37 PM

Figure 1: IBM Open Platform and BigInsights – cloud services.


The following report cites IBM as a leader : “The Forrester Wave™ : Big Data Hadoop Cloud Solutions, Q2 2016” which states :

“IBM differentiates BigInsights with end-to-end advanced analytics. IBM BigInsights runs atop IBM’s SoftLayer cloud infrastructure and can be deployed on any of 17 global data centers. IBM’s client relationships require it to be flexible in how it offers Hadoop in the cloud and offer highly customized configurations. IBM is making significant investments in Spark, offering data science notebooks that run with the platform. Enterprises using IBM’s data management stack will find BigInsights a natural extension to their existing data platform. The company has also launched an ambitious open source project, Apache SystemML, from its newly minted Spark Technology Center. IBM’s customers value the maturity and depth of its Hadoop extensions, such as BigSQL, which is one of the fastest and most SQL-compliant of all the SQL-for-Hadoop engines. In addition, BigQuality, BigIntegrate, and IBM InfoSphere Big Match provide a mature and feature-rich set of tools that run natively with YARN to handle the toughest Hadoop use cases.”

The report shows IBM scored among the highest in the solution configuration, data security, data management, development, cloud platform integration, ability to execute, road map, professional services, fixes and partnerships criteria.


In closing…

To conclude, there has never been a better time to invest in your BigInsight projects whether on-prem or in the cloud. The IBM Cloud First strategy is helping customers better manage their costs and focus on delivering business value and insight.  IBM can help abstract the complexities of managing infrastructures in a highly performing, highly available, security-rich and elastic scale-out environment across 17 worldwide multi-tenant data centers.  IBM BigInsights, combined with making data easy and our leadership and investment in Apache Spark, is helping deliver a next generation analytics platform capable of advanced analytics, machine learning, streaming, powerful SQL, graph analytics and more.

For more information on IBM BigInsights or to get started on BigInsights on Cloud click here.

Dinesh Nirmal – Vice President, Next Generation Platform, Big Data & Analytics on z

Follow me on Twitter:  @IBM_Innovation



TRADEMARK DISCLAIMER: Apache, Apache Hadoop, Hadoop, Apache Spark, Spark and the Spark logo are trademarks of The Apache Software Foundation.

IBM, IBM BigInsights, BigInsights are trademarks of the IBM Corporation.


Piotr Gnysinski: QA Wizard, Former Farmhand, and Family Man

We originally started the “You in the Private Cloud” series as a way to introduce our talented team to each other across our many geographies. I knew it was important for us to know each other as more than email addresses or voices during meetings.

But I didn’t realize at the time that it would become one of the favorite parts of my job. I truly love settling in for great conversations with the terrific people working on IBM Analytics offerings across the globe.

This time was no different. Many of you know that we have a vibrant presence in Krakow, Poland. And while there recently I got the chance to visit with Piotr Gnysinski who works as Test Lead on the Information Governance Catalog, a key part of our InfoSphere Information Server offering.


Piotr with Dinesh

Dinesh: I know you worked for a while for Comarch whose founder is Janusz Filipiak. Tell me about that experience.

Piotr: When I joined, Comarch was already a big company. It was my first job in IT and the first time I experienced emotions from customers coming our way: real people on the receiving end of my work — sometimes with real joyful reactions, sometimes with irritation as a result of bugs that made it through to the field.

I had to switch to real proactive thinking. I would say this attitude —this deep and strong engagement for customer advocacy and not just technical skills — is the most important single characteristic that can help someone do well in our business, or any business for that matter.

Dinesh: You’ve got a reputation for designing robust testing frameworks that cover a lot of ground. I think testing can seem like a mystery to many of us. Give me a sense of how you approach things.

Piotr: It depends on what you’re testing, but a big tool for us across the board is the idea of pair-wise testing. We know from studies that most defects can be discovered in tests of the interactions between the values of two variables (65-97%)[1]. A factor could be the browser vendor, the underlying operating system, and so on.

So, when you have an almost infinite number of tests you could run and very limited time, you first think of all those possible factors and figure out their possible values, then you classify these into groups called “equivalence classes”. You know that testing a single value from a class will probably give the same result as testing any other value in the group, so now you use algorithms that make sure each pair of classes is covered at least once — and you make sure to mix up which specific values are getting tested in the different pairs. That gives you good coverage.

I’ll send you a link to some information about Combinatorial Test Design if anybody wants to read up some more.


Piotr with wife Justyna, daughter Julia, and son Szymon


Dinesh: What do you do on weekends for fun?


Piotr: Almost every weekend, my wife Justyna and I take our son and daughter on some adventure: water park, bike riding, or visiting the playground. But my favorite is to bring them to visit Henrykow, which is a small village with about 30 people. My aunt and uncle have a farm and I used to go there every summer when I was a kid. I collected so many fantastic memories from there.

So now, whenever I have a chance, I pack up the family and two hours later we are in ‘Neverland’. They still keep livestock and they still work the land, so my kids get to see and do all that as well. For instance, not so long ago, they witnessed a calf being born, they very often get to ‘drive’ — being on my lap — a tractor, play in the hay for hours, or we go through the woods or the swamps, which always ends up with at least two of us all wet and muddy.


At the beach with friends and family 

Dinesh: It looks like you also make it to the gym once in a while. Am I crazy?

Piotr: Ha! Yes, I do weights mostly. There is something very satisfying in pushing yourself over imagined limits and doing completely exhausting training sessions, after which you can barely move. Yeah, gym is fun!

I’ll also get ideas for work at the gym, usually related to current work stuff: how are we going to approach creating our environment matrix for an upcoming release or how can we improve a process that was raised during a Lessons Learned session. Nothing revolutionary that would change the IT world, but very down-to-earth solutions that help us get better and better at what we do.

Dinesh Nirmal

Vice President, Analytics Development

Follow me on twitter @DineshNirmalIBM



Piotr’s hometown is  Bedzin, Poland, most famous for its castle.



Piotr: “A nearby roundabout, which was designed back when we had Communism here aiming to be perfect non-collision intersection for cars and trams. What we are left with, is this ’roundabout’ that is called ‘a kidney’ and where cars cross paths with trams three times before they leave it 🙂 It makes just about as much sense as Communism itself.”

Favorite programming language: JavaTM

Top 5 authors:

  1. Terry Pratchett
  2. Andrzej Sapkowski
  3. James Whitaker
  4. J.K. Rowling
  5. Wiktor Suworow

  1. IBM Haifa Research Laboratory Combinatorial Test Design (CTD)

Mihai Nicolae: Code Craftsman, Aspiring Chef and World Traveler

As much as I love meeting long-time IBMers and hearing their perspective on our evolution over the years, it’s a special pleasure to visit with our newer team members and to hear their visions for IBM’s future. You’ll remember my conversations with Martyna Kuhlmann, Ketki Purandare, and Phu Truong.

This time, I’m talking with Mihai Nicolae, a developer working out of our Markham office near Toronto. In just two years with IBM, Mihai has already been transformational on flagship products — Db2 , Watson Data Platform, and Data Science Experience. He’s currently trading time between DSX Local, IBM Data Platform, and the new Machine Learning Hub in Toronto.


Dinesh and Mihai

I hope you’ll take as much inspiration from our conversation as I did.

Dinesh: Where are you from originally?

Mihai: Romania. I’m very grateful — and always will be — for my parents having the courage to emigrate to Canada in their forties for me to have the opportunity to attend university here.

Dinesh: I bet they’re proud of you.

Mihai: Oh absolutely, I can’t ever have a doubt about that based on how much they talk about it.

Dinesh: If my son’s first job out of college was at IBM, I’d be proud, too. Tell me about your experience so far.

Mihai: I’ve been at IBM for two years full-time. Currently, I’m working on DSX Local and IBM Data Platform, which just started in January, after my time on the Db2 team. It’s been an amazing journey, especially GA-ing the product in only 4 months.

Dinesh: First of all, thanks and kudos to you and the team for delivering DSX in such a short amount of time. You’re now diving into machine learning. Did you take ML classes at university?

Mihai: I took one Intro-to-AI class, but frankly I feared the stats component of the ML course — and that 40% of my performance would depend on a 2-3 hour, stats-intensive exam.  At this point, I know that no hard thing is insurmountable if you put in the work.


Mihai at Big Sur.

Dinesh: Where do you see machine learning or data science going from here?

Mihai: I think it’ll be a vital component of every business. AI is the once-in-a-lifetime technology destined to advance humanity at an unprecedented scale. I think the secrets to defeating cancer, reversing climate change, and managing the global economy lie within the growing body of digital data.

But reaching that potential has to happen with the trust of end-users, trust in security and lack of bias. That’s why I think IBM will be a leader in those efforts: because IBMers really do value trust — I see it in the way we interact with each other day to day, as much as I see it in our interactions with clients. Trustworthiness is not something that can be compartmentalized.

Dinesh: Well said. I know you also work on encryption. Where does that fit in?

Mihai: When data is the core of everything, encryption is critical — encryption plus everything to do with security, including authentication and authorization. They’re all essential for earning and keeping user trust.

Dinesh: I love your passion for your work. Do you ever leave the office? What are your hobbies?

Mihai: Ha! I go to the gym, and I recently subscribed to one of those recipe services that delivers ingredients in pre-determined amounts. But traveling is really my fixation: California, Miami, Rhode Island and Massachusetts last year. And this year, I’ve been to the Dominican Republic, and then I head to Nova Scotia this summer.


…and at the Grand Canyon.

Dinesh: Nice. Do you have a particular dream destination?

Mihai: Thailand has a moon festival in April, where you get to have a water fight for three days. It’s the Thai new year. That might be my next big pick.

Dinesh: I travel a lot and I think there can be something really creative about travel, especially with the types of trips you’re talking about. I like asking developers whether they think of themselves as creative people. What’s your thought?

Mihai: Travel is definitely creative, but you’re making me think of the recipe service. I think of cooking from a card like learning programming from sample code: You get the immediate wow factor from building and running the working product but you don’t necessarily understand how and why the pieces fit so well together, or even what the pieces are. But over time, and with experience, you get understanding and appreciation. I think that’s when innovation and creativity can flourish.

Dinesh: Thanks, Mihai. Thanks for taking the time, thanks for the great work, and thanks for evolving IBM for our customers.

Dinesh Nirmal

Vice President Analytics Development

Follow me on twitter @DineshNirmalIBM


Home town: Constanta, Romania

Currently working on: DSX Local, Machine Learning Hub Toronto

Favorite programming language: Python

Top 5 future travel destinations:

  1. Thailand for Songkran
  2. Australia for scuba diving in Great Barrier Reef and surfing
  3. Brazil for Rio Carnaval
  4. Mexico for Mayan ruins and Diez y Seis
  5. Germany for Oktoberfest and driving on the Autobahn



Opening up the Knowledge Universe.

IBM Data Science Experience Comes to a Powerful, Open, Big Data Platform.

I have just finished presenting at the DataWorks Summit in San Jose. CA. where a partnership between IBM and HortonWorks was announced the aim of which is to help organizations further leverage their Hadoop infrastructures with advanced data science and machine learning capabilities. 

Some Background.

When Apache™ Hadoop® first hit the market there was huge interest in how the technology could be leveraged – from being able to perform complex analytics on huge data sets by using a cluster of thousands of cheap commodity servers and Map/Reduce  – to predictions that it would replace the enterprise data warehouse.  About three years ago Apache™ Spark™ gained a lot of interest unleashing a multi-purpose advanced analytics platform to the masses – a platform capable of performing streaming analytics, graph analytics, SQL and Machine Learning with a focus on efficiency, speed and simplicity.

I won’t go into details on the size of the Hadoop market, but many organizations invested heavily for numerous reasons including, but not limited to, it being seen as an inexpensive way to store massive amounts of data, the ability to perform advanced queries and analytics on large data sets with rapid results due to the Map / Reduce paradigm.  From one perspective, it was a data scientist’s dream to be able to reveal deeper insights and value from one’s data in ways not previously possible.

Spark represented a different but complementary opportunity allowing data scientists to apply cognitive techniques on data using machine learning – and other ways of querying data – in HDFS™ as well as data stored on native operating systems.

Many organizations including IBM made investments in Hadoop and Spark based offerings. Customers were enthused because these powerful analytics technologies were all based on open source representing freedom and low cost. Organizations including IBM participated in initiatives such as ODPi to help ensure interoperability and commonality between their offerings without introducing proprietary code.

Self-Service, Consumable, Cognitive tools.

Frustrated with IT departments not being able to respond fast enough to the needs of the business, departments sought a “platform” that would allow them to perform “self-service” analytics without having to be die-hard data scientists / engineers or developers.

The IBM Data Science Experience (DSX) emerged as a tool that could help abstract complexity, unify all aspects of data science disciplines regardless of technical ability to allow a single user or multiple personas to collaborate on data science initiatives on cloud, locally (on-prem) or while disconnected from the office (desktop).  Whether you prefer your favorite Jupyter notebook, R Studio, Python, Spark or a rich graphical UI that provides advanced users with all the tools they need – as well as cognitively guiding inexperienced users through a step by step process of building, training, testing, deploying a model – DSX helps unify many aspects into an end to end experience.

DSX Arch1
Figure #1 : Data Science Experience – Making data simple and accessible to all. 

Enterprise Ready.

A lot needs to happen for machine learning to be enterprise ready and robust enough to withstand business critical situations. Through DSX (see figure #1), advanced machine learning capabilities, statistical methods and advanced algorithms such as Brunel visualizations are available. Sophisticated capabilities such as automated data cleansing help ensure models are executing against trusted data. Deciding which parts of the data set are key to the predictive model (feature selection) can be a difficult task. Fortunately, this capability is automated as part of the machine learning process within DSX.  An issue that many data scientists face is the potential for predictive models to be impacted by rogue data or sudden changes in the market place.  IBM machine learning helps address this issue by keeping the model in its optimal state through a continuous feedback loop that can fine tune parameters of the model without having to take it off line.  This allows the model to sense and respond to each interaction (level of granularity defined by policy) without any human interaction.

A knowledge Universe – Unleashing Cognitive insights on Hadoop Data Lakes – with Power.

The potential of integrating the richness of DSX and the cognitive ML capabilities with all that data residing in HDFS (as well as many other data sources outside of Hadoop) is an exciting proposition for the data science community. It could help unlock deeper insights, increasing an organization’s knowledge about itself, the market, products, competitors, customers, sentiment at scale, at speeds approaching real time. One of the key features delivered as part of Hadoop 2.0 was YARN (yet another resource negotiator) that manages resources involved when queries are submitted to a Hadoop cluster, far more efficiently than in earlier versions of Hadoop – ideal for managing ever increasing cognitive workloads.

Simply put, I cannot think of a time where there has been a better opportunity for organizations to leverage their Hadoop investments until now.  The combination of Hadoop based technologies integrated with IBM ML and DSX unleashes cognitive insights to a very large Hadoop install base.

All very promising so far –but there is one more nugget to unleash that will help organizations with their cognitive workloads. IBM just announced HDF 3.0 for IBM Power Systems, bringing the built-for-big-data performance and efficiency of Power Systems with POWER8 to the edge of the data platform for streaming analytics applications.  This solution joins HDP for Power Systems, recently launched, which offers a 2.4X price-performance advantage [1] versus x86-based deployments.

I’m excited at the possibilities that lie ahead – how data scientists and machine learning experts might leverage and benefit from our offerings and the integration with Hadoop infrastructures – how they might take it to the next level in ways we’ve not yet imagined as we continue to enrich our offerings with more capabilities.

For more information on how to get started with Machine Learning click the link below :


Dinesh Nirmal – VP Analytics Development.  

Follow me on twitter @DineshNirmalIBM



IBM, the IBM logo,, IBM Elastic Storage Server, IBM Spectrum Scale, POWER8 and Power Systems are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their rst occurrence in this information with a trademark symbol (® or TM), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at

Apache Spark, Apache Hadoop, HDFS, Spark, Apache, Hadoop and the Spark, Hadoop logos are trademarks of The Apache Software Foundation.

Other company, product or service names may be trademarks or service marks of others.

1 – Based on IBM internal testing of 10 queries (simple, medium, complex) with varying run times, running against a 10TB DB on 10 IBM Power Systems S822LC for Big Data servers (20 C/40 T), 256GB memory, HDP 2.5.3, compared to published Hortonworks results based on the same 10 queries running on 10 AWS d2.8xlarge EC2 nodes (Intel Xeon E5-2676 v3), HDP 2.5. Individual results may vary based on workload size and other conditions.  Data as of April 20, 2017; pricing is based on web prices for the Power Systems S822LC for Big Data ( and HP DL380 Intel Xeon HP DL380; 20 C/40 T, 2 X E5-2630 v4; 256 GB found at

Meet Sebastian – A developer with a recipe for success

What a pleasure it was to meet Sebastian! He was recommended to me as a technical whiz with Python™ skills par excellence, but he impressed me just as much with his infectious, happy energy, his thinking on the advancement of society and technology, and how he chooses to spend his time sharing his passion for electronics and software with children and adults at his local community center. Sebastian hails from a close-knit village in the Ruhr Valley — perhaps that’s where he learned how to be effortlessly generous. Like all of you, I am constantly learning — not just about business or the next turn of the blade in machine learning, but about life, empathy, and leadership. More and more this year I’ve noticed the difference positive leadership makes. Sebastian, though a very young man, had much to teach me on this score.

When you walked into this room, you brought with you a burst of energy. I felt more positive as soon as we started talking — and I am already a very positive person. How do you do that?


By believing in a cause. Positivity is what we all need in life, and in business. If you are stretching yourself you’ll inevitably encounter failure and distress, but you have to stay positive. If we are talking about a group of people having a positive attitude, it doesn’t matter where you come from, or how old you are, it only matters that you all believe in the same cause.

What is the cause you believe in?

At work, it’s the team. We are all working on IBM DB2 Analytics Accelerator for z/OS, and I’ve never experienced a team that is as close together as this one, even though we are working on so many different parts. That’s the great aspect. When it comes to designing a new feature, we have to congregate and think about lots of different use cases. It’s not a simple product. Although we consider ourselves as writing “glue code,” we have to take special care with every little aspect and think through the consequences of potential failure. If I make a mistake in programming or designing a feature, it has a heavy impact on customers, and I know intimately what that can feel like from when I was in a customer-facing situation.

“Seeing someone learn and advance, and become an expert themselves, it’s the best thing that you can see. It lays the groundwork for society to advance.”

You started your career not long ago in customer support and now you’re a developer on a critical analytics product for large enterprise. What was it like, for a social person like you, to make the leap from facing customers to facing an Integrates Development Environment (IDE)?


It was natural. I did it using communications, and deep technical knowledge. I studied computer science at university, as a lot of people who work at IBM do, but we specialized in intercultural and international communications. We learned to communicate with passion and dedication, and to have empathy for other people and their needs and demands. My job in support was to understand the customer’s vision, and to show them that we at IBM are great partners to them. I also have deep technical knowledge, so now, knowing the architecture and where to expand it, that’s just awesome. But the foundation is the clients. They put so much trust in us that we have to give back to them.

Are you just as intensely involved with life outside of work? 

I’m interested in hardware, not just software: I love to lay out printed circuit boards and teach children how to solder and how to programmatically control it. It is a great balance to the complex software of my work life. With hardware, you can achieve simple things, like making an LED blink, and it makes children crazy with excitement.

You volunteer with children?

Absolutely! And adults. It’s great to see people learn and to share your knowledge, because sharing is what advances all of us. It helps me to find ways to explain what I know in different words. And, seeing someone understand what you just said, seeing someone learn and advance, and become an expert themselves, it’s the best thing that you can see. It lays the groundwork for society to advance.


For such a young person, you speak profoundly, and you are involved with noble causes: sharing your time and knowledge to move society forward. It maps exactly to what you do at work: using empathy and knowledge to advance the product. What do you do for downtime? Or is it all uptime?

Oh no! I love to do things with my friends. I am a baking enthusiast, and I frequently come to work on a Monday with lots of cookies and a big cake to share. I can relax if I bake. I love going to movies with friends and playing board games — that’s a great thing — and walks in nature. Nature helps me find my inner point of …


That is saying a little too much I think, but some peace, and calm.

Dinesh Nirmal,

Vice President Analytics Development

Follow me on twitter @DineshNirmalIBM

Name: Sebastian Muszytowski

Hometown: Ruhr Valley
Currently working on: IBM DB2 Analytics Accelerator for z/OS
Favorite Programming Language: Python™
Top 5 movies to see with friends:

1) Hedwig and the Angry Inch
2) Scott Pilgrim vs. The World
3) Juno
4) Little Miss Sunshine
5) Deadpool  

Sebastian’s Favorite New York Style White Chocolate Cheesecake with Blueberries.

200 g whole wheat cookies or Amaretti biscuits
100 g butter
250 g white chocolate
100 g crème fraîche (or heavy whipping cream)
600 g cream cheese
1 tbsp vanilla flavored sugar (or vanilla extract)
100 g powered sugar
a hand full of washed blueberries
How To:
0) Preheat your oven to 180°C or 350°F
1) Crumble the cookies (either by hand or in a food processor)
2) Melt the butter (short 10 seconds bursts in the microwave are fine for melting. Give it a good stir after each 10 second burst. Be cautious since butter in the microwave can become a huge mess if you heat it too quickly.)
3) Put some non-stick backing paper into the backing tin or use some butter or non-stick baking spray to cover the area of the baking tin.
4) Combine your crumbled cookies and the melted butter and put it in the baking tin to form the bottom of your cheesecake.
5) Put it in the oven for about 10 minutes and let it completely cool. (Hint: you do not need your oven any longer – you can turn it off ;-))
For the yummy cheesecake filling:
1) Chop up the chocolate in small pieces and mix it with the créme fraîche (or heavy whipping cream)
2) Heat it and stir it until it combines (I recommend short microwave bursts or a double boiler to do so)
3) In another bowl mix the creme cheese, vanilla sugar (or extract) and powered sugar until it is well combined
4) Slowly add the chocolate-creme-fraiche mixture into the bowl while you constantly stir.
5) Once it is combined (do not over-stir!) put it on top of your cooled cheesecake bottom, flatten the top and let it sit in the freezer for a while.
Decoration time!
1) Put some of the washed blueberries on top of the cheesecake to make it look even better. Be assured that it tastes even more delicious with them!
2) For an even better effect you can grate some left over white chocolate (if there is any) to make the cake even more attractive.

“Python” is a registered trademark of the Python Software Foundation.

The Data Scientist Who “Listens to the Problem”

My most recent in-flight reading was Thank You for Being Late. In it, Thomas Friedman says risk of AI isn’t that it’s going to take over humanity, HAL-like, but that we as humans could become so entranced by technology that we’ll neglect to teach it human values. It’s not machines v. humans or technology v. creativity. The more technology develops, the greater the opportunity to add to it our kindness, our fairness, and our creativity.

Jorge Castañon, Data Scientist at the IBM Machine Learning Hub and this week’s “You in the Private Cloud,” interviewee, clearly agrees. He and I met this week to discuss math, art, and the future of data science.

What was your first job at IBM?

To understand what data science is. There were so many different definitions! I decided it’s the combination of three things: mathematics, computation, and creativity. You need the creativity to listen to the problem and come up with the math. You need the math because data science requires a very deep understanding of the math that lies behind it. Then you need to compute the solution.

What do you mean, “Listen to the problem?”

Math is like a foreign language that not everyone can speak. When I’m listening to a problem, I’m translating from English to math and then translating back to English to continue the conversation.


The mathematics is distinct from the computation?

Yes. You think of a method mathematically. You eventually need to implement it in the computer: that’s the algorithm part. But first, it’s you and a blank piece of paper, and your thoughts, and eventually a math solution. The first person who thought about linear regression or least squares, that person was mathematical. It was a bunch of data points in space, and then, “Let’s find a model that fits those points” — but first it was math.

IBM was named on Gartner’s Magic Quadrant for Data Science Platforms for 2017, because of DSX with machine learning, and also the work you and the team are doing. A lot of it is side-by-side with clients: what’s that like?

It’s super fun! Learning about new problems is the best part of data science. The minute you start a conversation with a domain expert, to see what are the important parts of the model, what you can use for your math solution: that’s the exciting part. Talking to customers is a way to find the most interesting problems to solve.


I would imagine coming out of Rice with a PhD in Computational Mathematics that you had a lot of career choices. Why did you choose tech and why IBM?

Rice University is in Houston, so there were opportunities in the financial world and the energy sector and a lot of money to be made. I went to a conference and met IBM recruiters and got good indication of the spectrum of expertise at IBM. I felt I would be able to go wherever I wanted to in terms of the research and technical challenges; I would not be limited to one narrow role.

What’s the one thing about work that you are most excited about?

Collaboration. As a computational mathematician, you know a lot about certain things. But to go and talk about energy efficiency, or credit unions, or TV marketing, that gives me new topics where I can apply math and make a difference: to health care for example, or by making a building more efficient.

You are working at the edge of technology that doesn’t quite exist yet.

Definitely. My first project was to identify what is data science: that was unstructured. Then, how to use data science in our products: unstructured. How to apply machine learning: unstructured. It’s very exciting, to find the structure of things that are amorphous or not yet reified. And that’s what mathematics is. It goes back to my whole path, to the creative problem-solving that drives me.


Where do you see data science going? Is it part of the machine learning path, or will it diverge?

It’s an open question as to whether data science is going to be automated and humans won’t be needed. I think they will be.  The creativity aspect of data science cannot be automatic.

What do you do for fun outside of work?

I love art, and traveling with my wife: she’s also an applied mathematician. We got to museums and I take photographs of art. I used to do life drawing, but after the PhD and work — you get busy! I love James Turrell in particular; his work is based on what he called “the geometry of light” and he studied math in college.

Customers tell me it’s not just our skills they appreciate, it’s the commitment we make to their success, and they see that from working directly with you and IBMers like you. Thank you.

You are welcome. It is a pleasure to work here. I have a lot of space to grow.

Name: Jorge Castañon

Years at IBM: 3

Home town:  Mexico City

Currently working on: IBM Machine Learning Hub

All-time top five artists:

  1. Francisco Toledo
  2. James Turrell
  3. Willem De Kooning
  4. Mark Rothko
  5. M.C. Escher


Dinesh Nirmal,

Vice President Analytics Development

Follow me on twitter @DineshNirmalIBM