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Who Does What (And When) in MLOps?

Posted Oct 24, 2022 | Views 1.2K
# Maturity Level
# Team Agreement
# MLOps Journey
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Kevin Hartman
Practice Director Data Science & Machine Learning Engineering @ Unify Consulting

Kevin is a hands-on technology leader with experience in the design, development, and delivery of complex software systems. He is an experienced practitioner in data science, MLOps and machine learning engineering, application of emerging technologies, experimentation with causal inference, and lean product design.

Kevin has a master’s degree in Information and Data Science from UC Berkeley, and a bachelor’s degree in Electrical and Computer Engineering from the University of Illinois at Champaign-Urbana.

Kevin is also on the faculty at UC Berkeley where he teaches and advises students in the last class of the curriculum, Data Science Capstone.

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SUMMARY

Drive your ML like Mario Andretti. Get it operationalized.

Speed demon Mario Andretti is a racing legend. His success can be attributed to well-established routines that have been fully operationalized.

Operationalized you say? What does that mean?

It means every component of his racing career has been tuned for success. It includes a high-performance vehicle (tools), the support of a seasoned crew (people), and loads and loads of track time on tough-to-navigate courses (process).

MLOps defines the people, the process, and the tools to achieve Mario Andretti-like performance for your machine learning models.

In this talk Kevin Hartman, Practice Director of Data Science and Machine Learning Engineering at Unify Consulting, will dive into the “pit crew” of MLOps and take a look at who does what (and when) to make your models fully operational.

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