The MLOps course is about to explore various topics and tools needed for developing end-to-end ML solution. Below you can see the topic's we cover during this course.
- MLOps introduction - dependency management, code quality, Git, FastAPI, Docker
- Databases & file formats - PostgreSQL, DuckDB, Parquet
- Data processing - Polars
- Vector databases - pgvectorscale, SQLAlchemy, Milvus
- Versioning - DVC, MLFlow
- ML testing & data-centric AI - CleanLab, Giskard, Captum, SHAP
- Model optimization for inference - PyTorch optimization, ONNX, ONNX Runtime
- Monitoring & drift detection - Evidently, NannyML
- Introduction to cloud computing - AWS services
- Infrastructure as Code (IaC) - Terraform
- Deployment & CI/CD - GitHub Actions
- ML pipelines - Apache Airflow
- LLMOps - vLLM, Model Context Protocol (MCP), guardrails