Declarative MLOps - Streamlining Model Serving on Kubernetes // Rahul Parundekar// MLOps Meetup

MLOps Community Meetup #123! Last Wednesday, we talked to Rahul Parundekar, Founder of A.I. Hero, Inc.

//Abstract
Data Scientists prefer Jupyter Notebooks to experiment and train ML models. Serving these models in production can benefit from a more streamlined approach that can guarantee a repeatable, scalable, and high velocity. Kubernetes provides such an environment. And while third-party solutions for serving models make it easier, this talk demystifies how native K8s operators can be used to deploy models along with best practices for containerizing your own model, and CI/CD using GitOps.

// Bio
Rahul has 13+ years of experience building AI solutions and leading teams. He is passionate about building Artificial Intelligence (A.I.) solutions for improving the Human Experience. He is currently the founder of A.I. Hero - a platform to help you fix and enrich your data with ML. At AI Hero, he has also been a big proponent of declarative MLOps - using Kubernetes to operationalize the training and serving lifecycle of ML models and has published several tutorials on his Medium blog.

Before AI Hero, he was the Director of Data Science (ML Engineering) at Figure-Eight (acquired by Appen), a data annotation company, where he built out a data pipeline and ML model serving architecture serving 36 models (NLP, Computer Vision, Audio, etc.) and traffic of up to 1M predictions per day.

// Jobs board
https://mlops.pallet.xyz/jobs

// Related links
Website: https://aihero.studio
The Declarative MLOps Series:
  / streamlining-machine-learning-operations-w...  
  / containerizing-and-serving-an-ml-model-wit...  
  / continuous-integration-for-serving-ml-mode...  
  / continuous-delivery-of-ml-models-on-kubern...  

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Connect with Demetrios on LinkedIn:   / dpbrinkm  
Connect with Rahul on LinkedIn:   / rparundekar  

Timestamps:
[00:00] Musical introduction to Rahul Parundekar
[04:15] LLMs in Production Conference announcement
[04:36] Purchase our Swag shirt!
[06:45] Declarative Paradigm
[08:40] Why now?
[09:31] It's great for scalability
[10:01] Most MLOps tools work well with K8s
[11:00] Easy-deploys with tool-provided CRDs
[11:57] Caveats
[13:46] This talk
[14:09] 3 Ways to Serve ML Models
[14:14] Way 1: Serving a Model with an HTTP Endpoint
[15:08] Way 2: Serving the Model with a Message Queue
[15:43] Way 3: Long-running Task that Performs Batch Processing
[18:17] Buil your own container
[20:00] The main predictor (1/2): Singleton with load method
[20:23] The main predictor (2/2): Predict
[20:47] Way 1 5 steps
[23:54] Way 2 2 steps
[25:03] Way 3 2 steps
[26:00] Tests: Sanity check for the model
[26:53] Bringing it together: Entrypoint
[31:49] Continuous Integration (CI)
[34:35] Create docker-compose.yaml to make it easier for CI
[36:00] On PR: Run tests with Github Actions
[36:38] Branch-protection
[37:51] On PR: Github Actions automatically runs our test
[38:10] On PR: PRs can be then merged on approval
[38:28] Container Repository
[39:15] Continuous Integration (CI)
[39:26] On merge to main
[40:45] Actions that can constraint
[42:38] TODO
[43:17] Continuous Delivery
[45:42] Argo CD
[46:39] Image promotion with Kustomize
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