Machine Learning Orchestration and General Data Orchestration

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I think the biggest one is that people want to do everything. And there's this fine line of building something that is opinionated but not too constructive. I think we often have conversations internally about what do we build to make DAGS more attractive to machine learning engineers. There's so much in common between machine learning orchestration and general data orchestration.

Summary
Building a machine learning model one time can be done in an ad-hoc manner, but if you ever want to update it and serve it in production you need a way of repeating a complex sequence of operations. Dagster is an orchestration engine that understands the data that it is manipulating so that you can move beyond coarse task-based representations of your dependencies. In this episode Sandy Ryza explains how his background in machine learning has informed his work on the Dagster project and the foundational principles that it is built on to allow for collaboration across data engineering and machine learning concerns.
Interview

Introduction
How did you get involved in machine learning?
Can you start by sharing a definition of "orchestration" in the context of machine learning projects?
What is your assessment of the state of the orchestration ecosystem as it pertains to ML?
modeling cycles and managing experiment iterations in the execution graph
how to balance flexibility with repeatability
What are the most interesting, innovative, or unexpected ways that you have seen orchestration implemented/applied for machine learning?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on orchestration of ML workflows?
When is Dagster the wrong choice?
What do you have planned for the future of ML support in Dagster?

Contact Info

LinkedIn
@s_ryz on Twitter
sryza on GitHub

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

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