Bringing Feature Stores and MLOps to the Enterprise at Tecton
Data Engineering Podcast
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Navigating Machine Learning Features
This chapter explores the evolution of operational machine learning and the pivotal role of feature stores, with a focus on Uber's Michelangelo platform. It explains how features transform raw data into actionable insights for model predictions, highlighting the challenges and solutions in managing data within the ML lifecycle. The discussion includes a look at the Tekton platform's architecture, emphasizing its microservices design and integration with DevOps practices to improve feature lifecycle management.
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Summary
As more organizations are gaining experience with data management and incorporating analytics into their decision making, their next move is to adopt machine learning. In order to make those efforts sustainable, the core capability they need is for data scientists and analysts to be able to build and deploy features in a self service manner. As a result the feature store is becoming a required piece of the data platform. To fill that need Kevin Stumpf and the team at Tecton are building an enterprise feature store as a service. In this episode he explains how his experience building the Michelanagelo platform at Uber has informed the design and architecture of Tecton, how it integrates with your existing data systems, and the elements that are required for well engineered feature store.
Announcements
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- Your host is Tobias Macey and today I’m interviewing Kevin Stumpf about Tecton and the role that the feature store plays in a modern MLOps platform
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by describing what you are building at Tecton and your motivation for starting the business?
- For anyone who isn’t familiar with the concept, what is an example of a feature?
- How do you define what a feature store is?
- What role does a feature store play in the overall lifecycle of a machine learning project?
- How would you characterize the current landscape of feature stores?
- What are the other components that are necessary for a complete ML operations platform?
- At what points in the lifecycle of data does the feature store get integrated?
- What types of data can feature stores manage? (e.g. text vs. image/binary vs. spatial, etc.)
- How is the Tecton platform implemented?
- How has the design evolved since you first began building it?
- How did your work on Uber’s Michelangelo inform your work on Tecton?
- How has the design evolved since you first began building it?
- What is the workflow and lifecycle of developing, testing, and deploying a feature to a feature store?
- What aspects of a feature do you monitor to determine whether it has drifted?
- How do you define drift in the context of a feature?
- How does that differ from drift in an ML model?
- How do you define drift in the context of a feature?
- How does Tecton handle versioning of features and associating those different versions with the models that are using them?
- What are some of the most interesting, innovative, or unexpected projects that you have seen built with Tecton?
- When is Tecton the wrong choice?
- What do you have planned for the future of the product?
Contact Info
- kevinstumpf on GitHub
- @kevinstumpf on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
- Tecton
- Uber Michelangelo
- MLOps
- Feature Store
- Blog: What Is A Feature Store
- StreamSQL
- AWS Feature Store
- Logical Clocks
- EMR
- Kotlin
- DynamoDB
- scikit-learn
- Tensorflow
- MLFlow
- Algorithmia
- SageMaker
- Feast open source feature store
- Jaeger
- OpenTelemetry
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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