MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline.
The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardized formats to ensure cross-platform interoperability.
The repository covers a broad range of operational capabilities, including continuous integration and delivery automation, hierarchical configuration management, and system log aggregation. It also addresses observability through experiment tracking, training progress monitoring, and the use of dashboards to detect data drift during production inference.
The project is implemented using Jupyter Notebooks and provides configuration for linking virtual environments to notebook kernels.
