Table of contents

  1. MLOps - Concepts & Tools
  2. GitHub Labs
  3. MLflow Labs
  4. DVC Labs
  5. Airflow Labs
  6. Data Labeling Labs

MLOps

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

GitHub

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.

MLflow

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

DVC

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

Airflow

Lab Overview
Airflow Lab-1 Docker, Airflow-DAGs

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)