Model serving platform // Kyle Gallatin // Coffee #43 short clip

Coffee Sessions #43 with Kyle Gallatin of Etsy, Maturing Machine Learning in Enterprise.

"If depending on the resources you have, just evaluate a lot of vendors out there. There's a lot of really really great tools coming out of a lot of different companies. So many different enterprises offerings just to have model serving but it's also built as a part of a larger experiential platform."

//Abstract
The definition of Data Science in production has evolved dramatically in recent years. Despite increasing investments in MLOps, many organizations still struggle to deliver ML quickly and effectively. They often fail to recognize an ML project as a massively cross-functional initiative and confuse deployment with production. Kyle will talk about both the functional and non-functional requirements of production ML, and the organizational challenges that can inhibit companies from delivering value with ML.

// Bio
Kyle Gallatin is currently a Software Engineer for Machine Learning Infrastructure at Etsy. He primarily focuses on operationalizing the training, deployment, and management of machine learning models at scale. Prior to Etsy, Kyle delivered ML microservices and lead the development of MLOps workflows at the pharmaceutical company Pfizer. In his spare time, Kyle mentors data scientists and writes ML blog posts for Towards Data Science.

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// Takeaways
Data science is still poorly defined and there is a large variance in organizational maturity
Basically, everything we need for mature ML in modern organizations exists technically except for the strategy, mentality, organization, and governance
Organizations who poorly define data science often overburden their data scientists, but there are expectations that data scientists know some engineering
Operationalizing data science is not that different from software engineering, and software engineering can be one of the most valuable skillsets for a data scientist.

// Q&A with Kyle as a data science mentor: