MLOps Automation
Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.
MLOps Observability
Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.
MLOps Validation
Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
MLOps Industrialization
Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.
MLOps Initialization
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
MLOps Prototyping
Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.
MLOps Collaboration
Guide to prepare MLOps projects for sharing, collaboration, and community engagement.