Coffee Sessions #146

The Ops in MLOps - Process and People

Shalabh talks through their newfound appreciation for the MLOps perspective from a customer success standpoint. Shalabh's emphasis on setting realistic expectations and ensuring the delivery of promised value adds is particularly valuable. Generally, this episode provides a unique and insightful perspective on MLOps from the lens of customer success.

Take-aways

- Successful MLOps is much more than tooling - people, process aspects are critical - For companies embarking on MLOps or vendors selling MLOps solutions, considerations of business need, maturity and sophistication, etc. are key to success

In this episode

Shalabh Chaudhri

Shalabh Chaudhri

Head of Customer Success, Union AI

Shalabh has worked in the MLOps domain since 2020 at Algorithmia and Union AI. His experience spans startups, and small and large public companies. He has 10+ years of experience in the design, delivery, adoption, and business value realization from B2B infrastructure and platform solutions.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Abi Aryan

Abi Aryan

Host

Abi is a machine learning engineer and a live streamer. Over the past six years, her focus has been building machine learning models for various industries including short-form video hosting, OTT, e-commerce, insurance tech, etc., for startups across the US, UK, Canada, and India. Prior to that, she was a Data Science Fellow with Insight Toronto and a Visiting Research Scholar at UCLA working in AutoML, MultiAgent Systems, and Emotion Recognition. In her free time, she helps produce the MLOps Community Podcast.