MLOps Engineer Course: Azure AI-300 & GenAIOps Live Training

The first domain is pure environment building, and it is where sloppy setup costs marks later. Everything you deploy for the next seven weekends runs on what you build here.

• The Azure Machine Learning workspace and the four resources it provisions with it: storage, container registry, Key Vault and Application Insights
• Compute targets, instance types, clusters and quota planning
• Datastores, data assets, versioning and lineage
• Infrastructure as code with Bicep and the Azure CLI
• Identity, RBAC and network isolation for machine learning workloads
• Hands on: stand up a reproducible workspace entirely from code

The exam can test either the CLI v2 or the Python SDK v2 for the same declarative job, so this module teaches both flavours side by side rather than picking a favourite.

• Declarative job submission with the Azure ML CLI v2 and job YAML
• The Python SDK v2 for the same workflows, and when each one wins
• Curated and custom environments, dependency pinning and reproducibility
• Experiment tracking, metrics, artifacts and model signatures with MLflow
• Hyperparameter sweeps and distributed training jobs
• Pipelines that chain preparation, training and evaluation into one graph
• Hands on: a tracked, reproducible training pipeline you can rerun cleanly

This is the heaviest domain on the exam. It is also the part that separates a model that works from a model an organization is willing to depend on.

• Model registry, versioning, tagging and stage promotion
• Packaging, signatures and dependencies that survive the trip to production
• Approval gates and the evidence a reviewer needs before promotion
• Environment promotion from development to staging to production
• Rollback, model retirement and archival policy
• Hands on: promote a model through three environments with gates at each step

Endpoint questions are where scenario marks are won. The exam wants you to know which endpoint type a requirement implies and how to release without downtime.

• Managed online endpoints against batch endpoints, and the constraints that decide
• Traffic splitting, progressive rollout, blue green releases and instant rollback
• Autoscaling rules, instance sizing and concurrency limits
• Scoring scripts, inference environments and payload handling
• Authentication, private endpoints and securing the inference path
• Hands on: deploy a model, split traffic across two versions, then roll back under load

Automation is what makes the difference between an MLOps engineer and a data scientist who deploys occasionally. This module wires the whole lifecycle together.

• GitHub Actions workflows for training, evaluation and deployment
• Azure DevOps pipelines as the alternative path the exam also recognises
• Automated retraining triggered by schedule, by new data or by drift
• Model tests: data validation, performance thresholds and fairness checks as gates
• Secrets, service connections and workload identity federation
• Hands on: a pipeline that retrains, tests and promotes only when the thresholds pass

The domain that did not exist under DP-100 and that most courses on the market still do not teach. It is worth up to a quarter of the exam and it is the skill set AI teams are hiring for right now.

• Microsoft Foundry for model deployment, versioning and managed endpoints
• Prompt flow: authoring, versioning and running prompts as tested assets
• Evaluation flows scoring groundedness, relevance, coherence and safety
• Retrieval operations: index refresh, chunk strategy changes and regression testing
• Content safety, jailbreak resistance and abuse monitoring in the request path
• Token cost, latency and throughput as first class operational metrics
• Hands on: an evaluation and safety harness wired into a live generative feature

The final two domains together decide roughly a quarter of your score, and they are what keeps a production system trustworthy after launch.

• Data drift and prediction drift detection, with alerting and retrain triggers
• Azure Monitor, Application Insights and end to end tracing for inference
• Responsible AI dashboards, explanations and fairness reporting
• Performance tuning: batching, caching, right sizing compute and cost per prediction
• Quota management, throttling and capacity planning
• The full AI-300 outline reviewed in blueprint proportion, plus a full length domain scored mock and score report review

The Anthropic side of the program, aimed squarely at operations work rather than app building.

• Claude platform mastery: models, Projects, artifacts and tool use
• The Claude API and Claude Code for operations tooling and automation
• AI assisted runbooks: incident diagnosis, postmortem drafts and change summaries
• Prompt caching and batching for cost control at production volume
• Anthropic certification preparation with practice questions and a mock
• Capstone: one model and one generative feature, both fully monitored and automated end to end