Dinesh Nirmal

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The Five Pillars of Fluid ML

IBM Silicon Valley Lab
July 20, 2018 by Dinesh Nirmal in Machine Learning, Business of Tech

A few months ago, I was talking with the CTO of a major bank about machine learning. At one point he shook his head ruefully and said, “Dinesh, it only took me 3 weeks to develop a model. It’s been 11 months, and we still haven’t deployed it.”

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July 20, 2018 /Dinesh Nirmal
three pillars, Fluid ML, machine learning, algorithms, languages, data sets, tools, data, Jupyter, Zeppelin, continuous learning, data science, on-prem, cloud, multi-cloud, high-performance CPUs, O’Reilly Strata Data Conference, data pipelines, governance, Managed, Resilient, Performant, Measurable, Continuous
Machine Learning, Business of Tech
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Db2 for z/OS – Thirty-Five and Still Hip

IBM Silicon Valley Lab
April 24, 2018 by Dinesh Nirmal in Analytics, Business of Tech

Db2 for z/OS is one the most business-critical products in the IBM portfolio and remains core to many transaction processing, advanced analytics, and machine learning initiatives.

Looking back…

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April 24, 2018 /Dinesh Nirmal
Db2, z/OS, IBM portfolio, transaction processing, advanced analytics, machine learning, Sequel, SQL, online transactional processing (OLTP), IBM WebSphere, data, IBM Z analytics portfolio, continuous data, CPU, Internet of Things (IoT), smart meters, Cognos, SPSS, QMF, ML for z/OS, IBM DB2 Analytics Accelerator, A.I., M.L., Artificial Intelligence, Machine Learning for z/OS, data science, Data Gravity, IBM Data Science Experience, hybrid cloud, IBM Fellow
Analytics, Business of Tech
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Opening up the Knowledge Universe: IBM Data Science Experience Comes to a Powerful, Open, Big Data Platform

IBM SIlicon Valley Lab
June 13, 2017 by Dinesh Nirmal in Analytics, Business of Tech

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. 

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June 13, 2017 /Dinesh Nirmal
Apache™ Hadoop®, Machine Learning, ML, AI, Artificial Intelligence, streaming analytics, graph analytics, SQL, Apache™ Spark™, data, ODPi, IBM Data Science Experience (DSX), Jupyter notebook, R Studio, Python, Spark, DSX, IBM, Data Science Experience, Dinesh Nirmal, Cognitive Insights, Hadoop Data Lakes, IBM ML and DSX, IBM Analytics Development
Analytics, Business of Tech
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