mlops-python-package

Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.

Guide to prepare MLOps projects for sharing, collaboration, and community engagement.

Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.

Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.

Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.

Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.

Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.