Azure ML-Ops (Accelerator)
Getting Started
1. Project Accelerator Stages
2. Key Project Roles and Responsibilities
3. Mapping Roles to Project Accelerator Stages
STAGE 0: ML Ops Foundations
What is DevOps?
3. Introduction to planning efficient workloads with DevOps
4. Introduction to developing modern software with DevOps
5. Introduction to delivering quality services with DevOps
6. Introduction to operating reliable systems with DevOps
7. Adopting an Agile culture
8. What is Continuous Integration?
9. What is Continuous Delivery?
10. Automated Testing
11. Infrastructure as Code
12. What are Microservices?
13. Monitoring
What is ML Ops?
4. How is MLOps different from DevOps?
5. MLOps Maturity Model
6. Seven principles for machine learning DevOps
Skills, Roles & Responsibilities
5. Adopting a data science process framework
6. Managing the Data Science Process
7. Skilling Plan
STAGE 1: Design for ML Ops with AML
How Azure Machine Learning works: Architecture and concepts
2. MLOps: Model management, deployment, lineage and monitoring with Azure Machine Learning
Infrastructure Design
Technology Choices
1. AML Tool Selection Guide
2. Edge Deployment Technology Selection Criteria
AML - Ops Environment Design
1. AML Workspace Design Guide
2. Enterprise security and governance for Azure Machine Learning
3. Set up authentication for Azure Machine Learning resources and workflows
4. Manage access to an Azure Machine Learning workspace
Project Artefacts generated from Stage 1: Design
1. AML Infrastructure Design Checklist
STAGE 2: DEPLOY
Implementation Templates
Advanced template to create an Azure Machine Learning workspace
1. Reference ARM Templates
Azure Machine Learning Enterprise Terraform Example
Infrastructure Service Management
1. Cost Management
Project Artefacts generated from Stage 2: Deploy
STAGE 3: MIGRATE and OPERATE
Azure Machine Learning Acceleration Template
SECURITY.md
Automation
Migrating your Machine Learning code to Azure Machine Learning - Best Pratice
Prerequisites
Training
Inferencing
ML Pipelines
Automation
Commands to run this repo
Pipelines
Instructions
Key AzureML Concepts for Ops
Azure MLOps Best Practices
1. MLOps best practices with Azure Machine Learning
2. ParallelRunStep Performance Tuning Guide
Project Artefacts generated from Stage 3: Migrate and Operate
1. AML-Ops Process Flow
Additional References
Azure ML-Ops (Accelerator)
1-MLOps Foundations
2-SkillsRoles&Responsibilities
3-SkillingResources.md
Skilling Plan
Customized skilling plan for roles
Previous
2022,
Kohei Saito
Revision
57a0206
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