Meet up #10 Saurav Chakravorty sat down with us to talk about his vision of how MLOps reflect the old Indian story of blind men and an Elephant. As a lead data scientist at Brillo Saurav has build many MLOps pipelines and experienced using different ML platforms. He comes to talk with us about the difficulties of taking an ML platform from infancy to production and other key factors he has seen within the MLOps space.   

Today data science is a field that is an aggregation of people from various backgrounds - econometrics, statistics, engineering, business analysts, and data engineers. Each of these groups has different expectations from a Machine Learning platforms. But, each group faces problems that have some common challenges - improving reproducibility, reducing technical debt, reducing the time to try new experiments. The challenge before any MLOps system is to create platforms and processes that address the needs of each of these groups.   

Saurav is a tinkerer in the Machine Learning world with experience in the design and development of ML applications and processes.  In the past few years, he has been focused on improving the processes and tools around the Machine Learning teams. he explores the ideas of Auto ML, ML Ops, and model evaluation. He helps customers adopt and use the best tools and processes that allow them to scale their Data Science or Machine Learning tools. He has development experience in the open stack ML platforms and of late the managed ML services from Azure and AWS.  

You can read his article about creating your own MLOps pipeline with open source tools here: https://towardsdatascience.com/mlops-reducing-the-technical-debt-of-machine-learning-dac528ef39de  

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