Practical MLOps // Noah Gift // MLOps Coffee Sessions #27

Coffee Sessions #27 with Noah Gift of Pragmatic AI Labs, Practical MLOps // A “Gift” from Above This week, Demetrios and Vishnu got to spend time with the inimitable Noah Gift. Noah is a data science educator, who teaches at Duke, Northwestern, and many other universities, as well as a technical leader through his company Pragmatic AI Labs and past companies. // HOW is as important as WHAT In our conversation, Noah eloquently pointed out the numerous challenges of bringing ML into production, and especially for making sure it's used positively. It’s not enough to train great models; it’s important to make sure they impact the world positively as their productionized. How models are used is as important as what the model is. Noah specifically commented on externalities and how’s it incumbent on all MLOps practitioners to understand the externalities created by their models. // Just get certified As an educator, Noah has seen front and center how deficits in ML/DS education at the university level have led to the “cowboy” data scientist that doesn’t fit into an effective technical organizational structure. In his courses, Noah emphasizes getting started with off-the-shelf models and understanding how existing software systems are engineered before committing to building ML systems. Furthermore, Noah suggested getting certifications as a useful way of upskilling for anyone looking to increase their knowledge base in MLOps, especially by cloud providers. // Tech Stack Risk Finally, as many of you do, we debated the relative merits of the major cloud providers (AWS, Azure, and GCP) with Noah. With his vast experience, Noah made a great point about how adopting extremely new tools can sometimes go wrong. In the past, Noah adopted Erlang as a language used in the development of a product. However, as the language never quite took off (in his experience), it became a struggle to hire the right talent to get things done. Readers, as you go about designing and building t