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Complete guide to containerizing machine learning models and deploying them with Kubernetes for scalable, production-ready services.

Practical Python scripts to automate repetitive tasks, from file organization to API integrations and system monitoring.

Learn to build reliable, reproducible ML pipelines with proper versioning, monitoring, and deployment strategies.

Compare Git workflows: Feature Branch, GitFlow, Trunk-Based Development. Choose the right strategy for your team.

Set up continuous integration and deployment with GitHub Actions. Examples for testing, building, and deploying.