AI-300 Operationalizing Machine Learning and Generative AI Solutions

What's inside

AI-300 doesn't test whether you can train a model. It tests whether you can get models and generative AI apps into production safely, and keep them there.

This independent study guide for the Microsoft Certified: Machine Learning Operations Engineer Associate exam distils what AI-300 really expects you to understand into the comparisons, operational choices and traps that MLOps and GenAIOps decisions turn on. AI-300 replaced DP-100 in 2026, and the book is written for the new exam's focus on Azure Machine Learning and Microsoft Foundry operations.

Organised by technology

Eleven chapters, each readable on its own and together covering all five AI-300 skills areas:

• MLOps infrastructure: workspaces, registries, environments, datastores and data assets, compute, workspace RBAC, Bicep and Azure CLI, GitHub Actions with OpenID Connect, and network isolation.
• Model lifecycle: MLflow experiment tracking, automated ML, sweep jobs, distributed training and pipelines, model registration and Responsible AI, managed online and batch endpoints, safe rollout and rollback, and data drift monitoring.
• GenAIOps on Microsoft Foundry: resources and projects, identity and private networking, deployment types, provisioned throughput, model upgrade policies, and prompt variants and version control.
• Quality and optimisation: evaluation datasets and evaluators, automated evaluation, tracing and monitoring, RAG tuning and hybrid search, and fine-tuning with SFT, DPO and RFT.

Inside

• Comparison tables for every close call, such as which asset, which endpoint, which deployment type or which evaluator fits a requirement.
• 50+ Common trap callouts that name the answers that look right but aren't, and explain what's right instead.
• 10 diagrams, from registry sharing and blue-green rollout to deployment types, RAG tuning levers and the fine-tuning lifecycle.
• Key takeaways at the end of every chapter for fast revision.
• A glossary of 200+ terms, each linked to Microsoft Learn, with a free online version for print readers.

Up to date

Azure AI operations change quickly. This edition reflects Microsoft's documentation as of October 2026, including recent retirements such as low-priority VMs and the scheduled end of prompt flow, and notes where older material and today's product differ.

Understanding, not memorising

This book contains no exam questions. It explains the knowledge the exam expects, so you can answer questions you have never seen and apply the same judgement to real production systems.

Written by Tony Rough, a cloud architect with more than twenty years in IT infrastructure who holds the Azure Solutions Architect Expert, Azure Administrator and Azure Security Engineer certifications.

Part of the Ultra Transcenders series from Distilled Press. An independent publication, not affiliated with, sponsored by or endorsed by Microsoft Corporation.

Chapters

1. Azure Machine Learning workspaces, assets and compute
2. Automating and securing ML infrastructure
3. Training models and tracking experiments
4. Registering models, responsible AI and lifecycle
5. Deploying and monitoring machine learning models
6. The Microsoft Foundry platform: resources, identity and networking
7. Deploying foundation models
8. Prompt design, variants and version control
9. Evaluation and observability for generative AI
10. Optimising retrieval-augmented generation
11. Fine-tuning foundation models

Plus an appendix glossary of 200+ terms, each linked to Microsoft Learn, and free online for print readers.

Sample diagrams

Free study notes

Some sections of the book, free to read online:

• Azure Machine Learning workspaces, registries and asset types

  When to use a workspace, a registry, models, environments, components and data assets.

• Data assets and MLTable in Azure Machine Learning

  uri_file, uri_folder and mltable data assets, and how mltable.load() finds the MLTable file.

• Tracking experiments with MLflow in Azure Machine Learning

  Experiments, runs, parameters, metrics, artifacts and autologging.

• Hyperparameter tuning with sweep jobs

  Search spaces, sampling methods and early termination policies.

• Safe rollout and rollback on managed online endpoints

  Blue-green deployments, traffic splitting, mirroring and instant rollback.

• Microsoft Foundry deployment types: Global, Data Zone and Standard

  Where prompts are processed for each deployment type, and which one meets data residency needs.

• Provisioned throughput units (PTU) for Foundry models

  When to use provisioned deployments, how 429s and spillover work, and how PTUs are billed.

• Choosing evaluators for generative AI quality and safety

  Groundedness, relevance, coherence, fluency and risk and safety evaluators, and what each needs.