//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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Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kylegallatin/

// 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 skill sets for a data scientist.

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

https://www.youtube.com/watch?v=7byRQGHD39w&t=1s