Scale Your Data Science Teams With Machine Learning Operations Principles

Summary

Building a machine learning model is a process that requires well curated and cleaned data and a lot of experimentation. Doing it repeatably and at scale with a team requires a way to share your discoveries with your teammates. This has led to a new set of operational ML platforms. In this episode Michael Del Balso shares the lessons that he learned from building the platform at Uber for putting machine learning into production. He also explains how the feature store is becoming the core abstraction for data teams to collaborate on building machine learning models. If you are struggling to get your models into production, or scale your data science throughput, then this interview is worth a listen.

Announcements

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• Your host as usual is Tobias Macey and today I’m interviewing Mike Del Balso about what is involved in operationalizing machine learning, and his work at Tecton to provide that platform as a service

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by describing what is encompassed by the term "Operational ML"?
  • What other approaches are there to building and managing machine learning projects?
  • How do these approaches differ from operational ML in terms of the use cases that they enable or the scenarios where they can be employed?
• How would you characterize the current level of maturity for the average organization or enterprise in terms of their capacity for delivering ML projects?
• What are the necessary components for an operational ML platform?
• You helped to build the Michelangelo platform at Uber. How did you determine what capabilities were necessary to provide a unified approach for building and deploying models?
• How did your work on Michelangelo inform your work on Tecton?
• How does the use of a feature store influence the structure and workflow of a data team?
• In addition to the feature store, what are the other necessary components of a full pipeline for identifying, training, and deploying machine learning models?
• Once a model is in production, what signals or metrics do you track to feed into the next iteration of model development?
• One of the common challenges in data science and machine learning is managing collaboration. How do tools such as feature stores or the Michelangelo platform address that problem?
• What are the most interesting, unexpected, or challenging lessons that you have learned while building operational ML platforms?
• What advice or recommendations do you have for teams who are trying to work with machine learning?
• What do you have planned for the future of Tecton?

Keep In Touch

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Picks

• Tobias
  • Sandman graphic novel series by Neil Gaiman
• Mike
  • At Home: A Short History of Private Life by Bill Bryson

Closing Announcements

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Links

• Tecton
• Michelangelo
• sklearn
• Pandas
• Data Engineering Podcast Episode About StreamSQL
• Feature Store
• Master Data Management
• Amundsen
  • Data Engineering Podcast Episode
• Jupyter
• Algorithmia
• Unix philosophy
• Feast feature store
• Kubeflow
• Andreesen Horowitz Post On Emerging Data Architectures
• What is a feature store? post on the Tecton blog

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA