Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate
AI-300 Articles & Study Guides
Explore focused explanations and study guides for Operationalizing Machine Learning and Generative AI Solutions.
29 articles published
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01 · Study & Preparation · Guide
AI-300: Certification Scope and the AI Operations Role
Introduction
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Showing 1–12 of 29 articles
01 · Study & Preparation · Guide
AI-300: Certification Scope and the AI Operations Role
Introduction
02 · Study & Preparation · Guide
AI-300: Designing Workspaces, Compute, Assets, and Registries
Design Azure Machine Learning around environment-specific workspaces and explicitly versioned reusable assets.
03 · Study & Preparation · Guide
AI-300: Securing Azure Machine Learning Access and Networks
Secure Azure Machine Learning access by designing authorization and connectivity for each request separately. Microsoft Entra ID identifies the principal; Azure role-based access control (RBAC) governs allowed resource operations.
04 · Study & Preparation · Guide
AI-300: Lab: Automating an ML Workspace with Federated CI
Introduction
05 · Study & Preparation · Guide
AI-300: Choosing and Comparing Training Experiments
Use a custom Azure Machine Learning training job when you need direct control of the training logic, automated machine learning (AutoML) when a supported task fits an automated search across algorithms and configurations, and a sweep when…
06 · Study & Preparation · Guide
AI-300: Building Reproducible and Distributed Training Pipelines
Build an Azure Machine Learning training pipeline from versioned components whose inputs, outputs, code and environments are explicit.
07 · Study & Preparation · Guide
AI-300: Packaging and Qualifying Registered ML Models
Before treating a registered machine learning (ML) model as a release candidate, package its required dependencies, identify the exact model version, and connect that version to training and evaluation evidence.
08 · Study & Preparation · Guide
AI-300: Choosing Online and Batch Inference
Choose managed online inference when a caller needs a prediction in the response to its request and the model can finish within that interaction's latency budget.
09 · Study & Preparation · Guide
AI-300: Lab: Testing, Rolling Out, and Reverting an Online Model
Rehearse an online model release by keeping a known-good deployment available while testing a separate candidate, observing it with mirrored traffic, and moving ordinary requests in controlled stages.
10 · Study & Preparation · Guide
AI-300: Monitoring Drift, Actual Performance, and Retraining
Investigate when production data quality, input or prediction distributions, or measured model performance depart from an agreed expectation.
11 · Study & Preparation · Guide
AI-300: Provisioning Foundry Resources and Projects
Provisioning Foundry resources and projects establishes an application environment: a Foundry resource supplies the administrative boundary, a child project organizes development, and separately configured model deployments supply…
12 · Study & Preparation · Guide
AI-300: Securing Foundry Identities and Private Connectivity
Securing Foundry identities and private connectivity requires two cooperating controls: Microsoft Entra ID and role-based access control (RBAC) authorize an operation, while network configuration provides an allowed path to its…