What Does It Really Mean To Do MLOps And What Is The Data Engineer's Role?
Data Engineering Podcast
Is There a Need for More Efficient Resource Allocation?
Machine learning is becoming ubiquitous. It's just being imbedded in everything, all sorts of products. Something i'm thinkng about is we need to really focus our efforts in making that process more efficient. And so this other aspectof like understanding how to use the right resources, i think an important aspect of the tooling space. There's lots of systems that they don't enable you to do that by default.
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
- David
- @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
- MLOps Community
- Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz (affiliate link)
- MLOps
- DataOps
- DevOps
- The Sequence Newsletter
- Neptune.ai
- Algorithmia
- Kubeflow
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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