Azure ML-Ops (Accelerator)
  • SECURITY.md
Getting Started
  • 1. Project Accelerator Stages
  • 2. Key Project Roles and Responsibilities
  • 3. Mapping Roles to Project Accelerator Stages
ML Ops Foundations
  • MLOps Foundation Checklist
  • What is DevOps?
    • 1. Introduction to planning efficient workloads with DevOps
    • 2. Introduction to developing modern software with DevOps
    • 3. Introduction to delivering quality services with DevOps
    • 4. Introduction to operating reliable systems with DevOps
    • 5. Adopting an Agile culture
    • 6. What is Continuous Integration?
    • 7. What is Continuous Delivery?
    • 8. Automated Testing
    • 9. Infrastructure as Code
    • 10. What are Microservices?
    • 11. Monitoring
    • /1-MLOpsFoundation/0-DevOpsOverview/media/
  • What is ML Ops?
    • 1. How is MLOps different from DevOps?
    • 2. MLOps Maturity Model
    • 3. Seven principles for machine learning DevOps
  • Skills, Roles & Responsibilities
    • 1. Adopting a data science process framework
    • 2. Managing the Data Science Process
    • 3. Skilling Plan
Design and Provision AML Infrastructure
  • Design and Provision AML Infrastructure Checklist
  • 1. Architecture and Design Concepts of Azure Machine Learning
  • 2. Model management, Deployment, Lineage and Monitoring with Azure Machine Learning
  • Infrastructure Service Management
    • 1. AML Tool Selection Guide
    • 2. Edge Deployment Technology Selection Criteria
    • 3. Enterprise security and governance for Azure Machine Learning
    • 4. Set up authentication for Azure Machine Learning resources and workflows
    • 5. Manage access to an Azure Machine Learning workspace
    • 6. Cost Management
AML Infrastructure Deployment
  • AML Infrastructure Deployment Checklist
  • 1. Setup Local Environment for Development
  • 2. Organize and set up Azure Machine Learning environments
  • 3. Creating Separate Environments for Development
  • Advanced template to create an Azure Machine Learning workspace
  • Azure Machine Learning Enterprise Terraform Example
Migrate and Operate
  • 1. Key AzureML Concepts for Ops
  • 2. MLOps best practices with Azure Machine Learning
  • 3. ParallelRunStep Performance Tuning Guide
  • /4-Migrate/dstoolkit-mlops-base/
    • SECURITY.md
    • Azure pipeline folder
    • Notebook Directory
    • Operation directory
    • Source folder
Azure ML-Ops (Accelerator)
  • 3-Deploy
  • checklist.md

AML Infrastructure Deployment Checklist

For continuous integration and development (CI/CD) of your Machine Learning services development:

  • Have you setup your local environment to connect to your AML workspaces?

  • Have you organised workspaces for the different teams/projects in Azure ML?

  • Have you designed and deployed separate environments for Dev, Test, PROD

  • Are you able to deploy chances using the automated deployment pipelines?

  • Have you deployed Azure ML service and related services on Azure?

  • Have you automated the deployment of these services using Infrastructure-as-code (IaC)

ADDITIONAL INFO: Your team can accelerate the migration of your existing model to AML by referring to the materials and templates here.

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