MLOps Labs

This repository contains a set of exercises developed during course on AGH UST based on that repository: https://github.com/j-adamczyk/MLOps_course_AGH

Content

Lab 01 - MLOps introduction - dependency management, code quality, Git, FastAPI, Docker

Lab 02 - Databases & file formats - PostgreSQL, DuckDB, Parquet

Lab 03 - Data processing - Polars

Lab 04 - Vector databases - pgvectorscale, SQLAlchemy, Milvus

lab 05 - Versioning - DVC, MLFlow

lab 06 - ML testing & data-centric AI - CleanLab, Giskard, Captum, SHAP

lab 07 - Model optimization for inference - PyTorch optimization, ONNX, ONNX Runtime

lab 08 - Monitoring & drift detection - Evidently, NannyML

lab 09 - Introduction to cloud computing - AWS services

lab 10 - Infrastructure as Code (IaC) - Terraform: https://github.com/wozniakos10/MLOps-lab10

lab 11 - Deployment & CI/CD - GitHub Actions: https://github.com/wozniakos10/MLOps-lab11

lab 12 - ML pipelines - Apache Airflow

lab 13 - LLMOps - vLLM, Model Context Protocol (MCP), guardrails