Table of contents


Blog Overview
1. MLOps Introduction
2. Scoping The Importance of Scoping in MLOps: A Guide for ML Engineers
3. Introduction to GitHub Basics, Terminology
4. Docker Container Importance, Components, and Benefits
5. Experiment Tracking Importance, Components, and Benefits
6. MLflow MLflow - Experiments and Runs
6. Data Version Control(DVC) Introduction to DVC
7. Airflow Introduction to Airflow


Lab Overview
GitHub Lab-1 GitHub Actions and Workflows, tests with pytest and unittest
GitHub Lab-2 GitHub Actions - Automating model training, storing and versioning.


Lab Overview
MLflow Lab-1 MLflow - Autolog, Saving and Loading Model
MLflow Lab-2 MLflow - Model Registration, Batch Inference


Lab Overview
DVC Lab-1 Using DVC with Google Cloud Platform(GCP)


Lab Overview
Airflow Lab-1 Docker, Airflow-DAGs
Airflow Lab-2 Airflow-DAGs, E-mail notifications, FastAPI

Data Labeling

Lab Overview
Data Labeling Lab-1 Snorkel - Labeling Functions(LF)
Data Labeling Lab-2 Snorkel - Transformation Functions(TF)
Data Labeling Lab-3 Snorkel - Slicing Functions(SF)


Lab Overview
Docker Lab-2 Docker Container
Docker Lab-1 Docker conatiner


Lab Overview
Flask_GCP_lab Flask - API


Lab Overview
FastAPI Lab-1 FastAPI