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Solving the ML Production Puzzle: A Practical Guide to Modern MLOps

A hands-on workshop covering the complete journey from ML model development to production deployment using industry-standard tools.

Overview

This 6-hour intensive workshop teaches you to deploy and maintain machine learning models in production. Through progressive hands-on modules, you'll work with Kubernetes, MLflow, BentoML, and Kubeflow to build production-ready ML systems.

What You'll Learn:

  • Model versioning with Hugging Face Hub and MLflow
  • Containerization with Docker and BentoML
  • Kubernetes deployment and scaling
  • Production monitoring and observability
  • CI/CD pipelines for ML models

Setup Options:

  • Local (macOS): Python 3.11+, Docker Desktop, kubectl, kind
  • GitHub Codespaces: Pre-configured cloud environment

Who Am I!

https://www.linkedin.com/in/rabieh-fashwall/

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Start Here

https://github.com/rfashwall/ml-con-workshop/wiki

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  • Python 61.1%
  • Shell 22.1%
  • Go 12.4%
  • Dockerfile 4.4%