Martyna Kuhlmann: DB2 Regression Tester and Artist

May 12, 2017 by Dinesh Nirmal in People

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.

Sebastian Muszytowski: A Developer with a Recipe for Success

IBM Silicon Valley Lab

April 13, 2017 by Dinesh Nirmal in People

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.

Jorge Castañon: The Data Scientist Who “Listens to the Problem”

IBM Silicon Valley Lab

March 14, 2017 by Dinesh Nirmal in People

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.

IBM Machine Learning for z/OS – Like No Other

IBM Silicon Valley Lab

February 13, 2017 by Dinesh Nirmal in Machine Learning, Business of Tech

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.

Ketki Purandare: Database and Machine Learning Expert, Lead Vocalist on “Dance with Me”

IBM Silicon Valley Lab

February 08, 2017 by Dinesh Nirmal in People

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.

Welcome to the Private Cloud

IBM Silicon Valley Lab

January 31, 2017 by Dinesh Nirmal in Machine Learning, Business of Tech

Readers of this blog know that I like to imagine the world through the eyes of my young son. 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.

Morgan Fritz: Industrial Design Grad, Fashion Designer, DSX Veteran

IBM Silicon Valley Lab

January 13, 2017 by Dinesh Nirmal in People

Our Chief Designer, David Townsend, takes new designers aside when they join our team and tells them, “This is the most complicated thing you’ll ever design. Don’t be afraid to ask dumb questions. And know that everything else in your career you design after this will be easy compared to what you’re about to do.”

Phu Truong: Humble Leader, Loves Logic, Hates Calculations

IBM Silicon Valley Lab

January 04, 2017 by Dinesh Nirmal in People

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.

Kewei Wei: Lead Developer, China Native, History Buff

IBM Silicon Valley Lab

December 13, 2016 by Dinesh Nirmal in People

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.

Should’ve, Could’ve, Would've - Making the Optimal Decision

IBM Silicon Valley Lab

November 28, 2016 by Dinesh Nirmal in Machine Learning, Business of Tech

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.

Using IBM Machine Learning to Help Solve Real World Business Problems

IBM Silicon Valley Lab

October 05, 2016 by Dinesh Nirmal in Machine Learning, Business of Tech

Billions of connected devices, zetabytes of data, power and brand loyalty now in the hands of the consumer, businesses having to market and sell to each and every one of us. What just happened? Three driving forces – mobile, cloud and a continuing explosion of data. Everything just got personal.

IBM BigInsights Basic Plan Ready for a Successful Journey

IBM Silicon Valley Lab

September 08, 2016 by Dinesh Nirmal in Analytics, Business of Tech

It seems to me that more and more companies are adding Apache Hadoop to their mix of technologies in attempts to spend less time in the creation of an Hadoop infrastructure and focus more on gaining the benefits. IBM BigInsights (our implementation of Hadoop) is ready to take on that challenge. It provides a complete solution that is not only open and combines Hadoop and Spark, but also provides the right tools to help make Big Data easier and more scalable, while integrating it seamlessly with SQL, noSQL and other data types.

Apache Spark Continues to Forge Ahead with Help from IBM’s Spark Technology Center

IBM Silicon Valley Lab

August 16, 2016 by Dinesh Nirmal in Analytics, Business of Tech

Apache Spark™ puts both deep and broad advanced analytics capabilities in the hands of the masses. Whether a data scientist, data engineer, analytics app developer or citizen analyst – Spark delivers sophisticated analytics simpler, faster and more efficiently than ever before.

Machine Learning for the Enterprise

IBM Silicon Valley Lab

August 06, 2016 by Dinesh Nirmal in Machine Learning, Business of Tech

In my last blog “Business differentiation through Machine Learning” I introduced and described the concepts of machine learning. We traced its origins from a computer science project to Watson showcasing and winning on the Jeopardy TV quiz show and its real world use across numerous industries including health care.

Business Differentiation through Machine Learning

IBM Silicon Valley Lab

June 02, 2016 by Dinesh Nirmal in Machine Learning, Business of Tech

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.

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

IBM

May 13, 2016 by Dinesh Nirmal in Analytics, Business of Tech

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.