Gartner Blog Network

Sumit Agarwal
Sr Director Analyst
1 year at Gartner
24 years IT Industry

Sumit Agarwal provides guidance on Artificial Intelligence (AI), Machine Learning (ML), Data Science Architectures, Data Management and Data Integration architecture and strategies, based on upcoming ideas, current trends, and past project implementations. Read Full Bio

Machine Learning – Shift from Modeling to Engineering

by Sumit Agarwal  |  August 21, 2020

In 2012, Harvard Business Review had published Data Scientist as the sexiest job of 21st century. In 2019, an Indeed survey had Computer Vision Engineer and Machine Learning Engineer as the highest paid tech jobs, ahead of the data scientist. Within seven years, the balance has shifted from modeling to implementation. From focused ML problems […]

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5 Techniques to Troubleshoot a Machine Learning Model

by Sumit Agarwal  |  August 5, 2020

Troubleshooting code in C++ or Java has been fairly easy. With a plethora of tools that help with a step-by-step execution of code to thread analysis at run time to de-compilation of code,  software developers probably take a debug or troubleshooting capability for granted. Data Scientists and other Machine Learning professionals still need to debug […]

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Robust Machine Learning needs Data and a Lot More

by Sumit Agarwal  |  May 27, 2020

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” Sir Arthur Conan Doyle, Sherlock Holmes My aha moment came when I was working on a proof of concept using the MNIST dataset. The dataset includes a curated set […]

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