Practitioners Guide to MLOps // Donna Schut and Christos Aniftos // Coffee Sessions #82
MLOps.community
MLOps
Christos: We use the practical sky to MLOps as kind of the starting point. He says selecting the right carefully is a super important piece because it helps you scope the piece of work. Christos: The most important here is that I still need to train my service, right? But yes, once or twice a year isn't enough; we have to talk about new products too.
MLOps Coffee Sessions #82 with Donna Schut and Christos Aniftos, Practitioners' Guide to MLOps.
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// Abstract
The "Practitioners Guide to MLOps" introduced excellent frameworks for how to think about the field. Can we talk about how you've seen the advice in that guide applied to real-world systems? Is there additional advice you'd add to that paper based on what you've seen since its publication and with new tools being introduced?
Your article about selecting the right capabilities has a lot of great advice. It would be fun to walk through a hypothetical company case and talk about how to apply that advice in a real-world setting.
GCP has had a lot of new offerings lately, including Vertex AI. It would be great to talk through what's new and what's coming down the line. Our audience always loves hearing how tool providers like GCP think about the problems customers face and how tools are correspondingly developed.
// Bio
Donna Schut
Donna is a Solutions Manager at Google Cloud, responsible for designing, building, and bringing to market smart analytics and AI solutions globally. She is passionate about pushing the boundaries of our thinking with new technologies and creating solutions that have a positive impact. Previously, she was a Technical Account Manager, overseeing the delivery of large-scale ML projects, and part of the AI Practice, developing tools, processes, and solutions for successful ML adoption. She managed and co-authored Google Cloud’s AI Adoption Framework and Practitioners' Guide to MLOps.
Christos Aniftos
Christos is a machine learning engineer with a focus on the end-to-end ML ecosystem. On a typical day, Christos helps Google customers productionize their ML workloads using Google Cloud products and services with special attention to scalable and maintainable ML environments.
Christos made his ML debut in 2010 while working at DigitalMR, where he led a team of data scientists and developers to build a social media monitoring & analytics tool for the Market Research sector.
// Related links:
Select the Right MLOps Capabilities for Your ML Use Case
https://cloud.google.com/blog/products/ai-machine-learning/select-the-right-mlops-capabilities-for-your-ml-use-case
Practitioner's Guide to MLOps white paper
https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
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Timestamps:
[00:00] Introduction to Donna Schut and Christos Aniftos
[05:52] Inspiration of Practitioner's Guide to MLOps paper
[06:57] Model for working with customers
[08:14] Where are we at MLOps?
[10:20] Working with customers
[11:30] Practitioner's Guide to MLOps paper
[16:16] Training maturity levels
[22:37] Context about the discovery process
[25:21] Disciplines and security
[26:12] Is there a level up in maturity?
[29:50] Successes or failures that stand out
[38:00] War stories
[43:16] Wrap up
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