Putting Machine Learning into Production: An Overview — Srijith Rajamohan, Databricks

Machine learning as a decision-making tool has gained wide acceptance. However, in order to extract information from data using machine learning, it is critical that the ML lifecycle be managed using best practices. In this talk, I will cover some of the foundational principles in scalable machine learning starting from data provenance and lineage and model building to model deployment and monitoring.
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Dr. Srijith Rajamohan is a senior developer advocate at Databricks where he works on data science and machine learning problems. He is interested in all things related to the ML lifecycle and has a focus on natural language processing and Bayesian modeling. He recently launched a series of three courses on Coursera to teach computational statistics and Bayesian inference using PyMC3. He was a computational scientist from 2014 to 2020 at Virginia Tech, where he worked on natural language understanding using deep learning techniques, Bayesian inference and reproducible/scalable infrastructure for ML problems.

​He is a graduate of the University of Tennessee SimCenter: Center of Excellence in Applied Computational Science and Engineering, where he did his thesis work on computational electromagnetics using the finite-element method and earned a doctorate in computational engineering in 2014. He also holds a master’s in electrical engineering from Pennsylvania State University, where he worked on implementing neural networks for computer vision on the IBM cell processor.

https://www.ischool.berkeley.edu/events/2022/putting-machine-learning-production-overview

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