Summary

Putting machine learning models into production and keeping them there requires investing in well-managed systems to manage the full lifecycle of data cleaning, training, deployment and monitoring. This requires a repeatable and evolvable set of processes to keep it functional. The term MLOps has been coined to encapsulate all of these principles and the broader data community is working to establish a set of best practices and useful guidelines for streamlining adoption. In this episode Demetrios Brinkmann and David Aponte share their perspectives on this rapidly changing space and what they have learned from their work building the MLOps community through blog posts, podcasts, and discussion forums.

Announcements

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Your host is Tobias Macey and today I’m interviewing Demetrios Brinkmann and David Aponte about what you need to know about MLOps as a data engineer

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what MLOps is?
- How does it relate to DataOps? DevOps? (is it just another buzzword?)
What is your interest and involvement in the space of MLOps?
What are the open and active questions in the MLOps community?
Who is responsible for MLOps in an organization?
- What is the role of the data engineer in that process?
What are the core capabilities that are necessary to support an "MLOps" workflow?
How do the current platform technologies support the adoption of MLOps workflows?
- What are the areas that are currently underdeveloped/underserved?
Can you describe the technical and organizational design/architecture decisions that need to be made when endeavoring to adopt MLOps practices?
What are some of the common requirements for supporting ML workflows?
- What are some of the ways that requirements become bespoke to a given organization or project?
What are the opportunities for standardization or consolidation in the tooling for MLOps?
- What are the pieces that are always going to require custom engineering?
What are the most interesting, innovative, or unexpected approaches to MLOps workflows/platforms that you have seen?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on supporting the MLOps community?
What are your predictions for the future of MLOps?
- What are you keeping a close eye on?

Contact Info

Demetrios
- LinkedIn
- @Dpbrinkm on Twitter
- Medium
David
- LinkedIn
- @aponteanalytics on Twitter
- aponte411 on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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