AI & ML Academy - ML Engineering in Production (MLOps)
Welcome to the AI & ML Academy (AIA) - ML Engineering in Production (MLOps)!
Machine learning Operations (MLOps) applies DevOps principles and techniques to machine learning projects and helps in efficiently scaling your project from experimentation to production. This module illustrates key DevOps concepts such as source control, automation, and CI/CD to build an end-to-end MLOps solution using popular tools such as Azure DevOps and GitHub Actions.
Getting Started - A Journey, not a Destination!
- Introduction to MLOps – This is a great resource if you’re new to MLOps!
MLOps with Azure DevOps
- Azure MLOps (v2) Solution Accelerator – This accelerator serves as the starting point for MLOps implementation in Azure using Azure DevOps.
- MLOps From Scratch hack using Azure DevOps – This challenge-based hack will introduce you to the Azure DevOps tooling for MLOps and will help you gain hands-on experience with creating end-to-end Build & Release pipelines to continuously train and deploy your ML model(s).
MLOps with GitHub Actions
- End-to-end MLOps with Azure ML and GitHub Actions – This provides a learning path that will help you gain hands-on experience with Azure Machine Learning and GitHub Actions.
MLOps with Kubeflow
- End-to-End Pipeline Example on Azure – This guide takes you through using your Kubeflow deployment to build a machine learning (ML) pipeline on Azure.
MLOps with mlFlow
- Getting Started with MLflow
- MLflow Documentation – The latest on mlFlow.
- Track ML models with MLflow and Azure Machine Learning
- Databricks Academy