JITA Learning · Microsoft · Field Guide
AI-300 · Operationalizing Machine Learning and Generative AI Solutions
Build working judgment for MLOps and generative AI production operations.
What you should expect
Build working judgment for MLOps and generative AI production operations.
This Field Guide develops a working mental model of MLOps and generative AI production operations. You will connect manage workspaces, projects, experiments, models, environments, and deployment assets, use MLflow and lifecycle evidence to compare experiments and model performance, design CI/CD and infrastructure automation for repeatable AI releases, and choose deployment, scaling, endpoint, and throughput approaches for production workloads. You are not just memorizing feature names; you are learning what owns the requirement, what can fail, and how to verify the outcome.
What you will learn
- manage workspaces, projects, experiments, models, environments, and deployment assets
- use MLflow and lifecycle evidence to compare experiments and model performance
- design CI/CD and infrastructure automation for repeatable AI releases
- choose deployment, scaling, endpoint, and throughput approaches for production workloads
- recognize why one technically plausible alternative loses under the stated requirement
- apply Changed Fact reasoning and verify the design or implementation through operational evidence
Decision skills you will practice
- identify the governing requirement
- compare the nearest plausible alternative
- apply Changed Fact reasoning
- trace prerequisites, dependencies, and failure paths
- verify configured state, effective state, runtime outcome, and negative-path evidence
Who this is for
AI engineers, ML engineers, and platform practitioners operationalizing machine learning and generative AI workloads
Use this when you need to learn the architecture, mechanics, dependencies, tradeoffs, failure paths, and verification logic behind the certification.
Decisions you will learn to distinguish
- model-quality issue vs deployment/runtime issue
- experiment tracking vs production monitoring
- successful deployment vs safe, observable, supportable operation
Why JITA is different
Microsoft explains the platform. JITA teaches the decision.
Microsoft Learn explains the services, features, and documented behavior that support MLOps and generative AI production operations. JITA connects those capabilities into decisions: what owns the requirement, what the nearest alternative is, what changes when one fact changes, and what evidence proves the implementation worked.
How to choose:
Use the Field Guide to build understanding. Use the Exam Preparation Guide to test whether that understanding survives exam-style decisions.