MLOps.community Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125
MLOps Coffee Sessions #125 with Rafael Pierre, Deploying Real-time ML Models in Minutes with Databricks Model Serving V2, co-hosted by Ryan Russon.
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// Abstract
From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-time REST APIs for serving ML models.
With Databricks Model Serving V2, we introduce the idea of Serverless REST endpoints to the platform. This allows teams to easily deploy their ML models in a production-grade platform with a few mouse clicks (or lines of code 😀).
// Bio
Rafael has worked for 15 years in data-intensive fields within finance in multiple roles: software engineering, product management, data engineering, data science, and machine learning engineering.
At Databricks, Rafael has fun bringing all these topics together as a Solutions Architect to help our customers become more and more data-driven.
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// Related Links
https://mlopshowto.com
Airflow Summit 2022:
https://youtu.be/JsYEOdRBgREING
Data Engineering Meetup:
https://www.youtube.com/watch?v=gJoxX1rRZJI
MLOps World Virtual Summit NYC 2022:
https://drive.google.com/file/d/1EXsqmLfrPAsV9i6h6pGfJxVjMO9y6u9a/view?usp=sharing
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Connect with Rafael on LinkedIn: https://www.linkedin.com/in/rafaelpierre
Timestamps:
[00:00] Rafael's preferred coffee
[00:14] Intro and background of Ryan
[01:18] Takeaways
[04:15] Rafael's first job and first touch in data
[06:35] IOT with no cloud
[08:00] Rafa's personal experience with Kubernetes
[09:16] Hardest thing to grasp when starting
[10:49] ML in Kubernetes
[13:32] Why is Kubernetes a good platform for ML?
[15:06] Is Kubernetes always the right answer?
[19:30] Recap of Ale Solano's background
[20:30] Cultural side of MLOps
[23:07] Ryan's experience
[25:04] Databricks over Kubernetes
[27:20] What to gain from Databricks?
[29:28] Best way to structure and promote code
[32:13] Rationalizing tradeoffs
[34:08] Difference from a mature software org
[35:26] Best way to deal with artifacts
[38:21] RBAC is a must-have
[41:10] Favorite tools for modeling
[42:19] Wrap up