MLOps on Azure: From Data Science to Deployment

Course Description

This SkillDynamo course develops practical, role-relevant capability in building governed Azure machine-learning lifecycles from experimentation to deployment, monitoring, and retraining. The learning path progresses from essential concepts and planning to implementation, validation, troubleshooting, and operational use.

Learners work through realistic scenarios, guided demonstrations, applied exercises, and evidence-based reviews. Every topic is connected to workplace responsibilities, common risks, quality controls, and measurable outcomes.

The course helps professionals make informed decisions, identify failure points, communicate results, and create an actionable improvement plan for future projects.

What You Will Learn

• Configure workspaces, compute, data assets, environments, identity, storage, networking, and permissions
• Track code, data, parameters, metrics, artifacts, lineage, and reproducible experiments
• Build reusable training components, pipelines, hyperparameter searches, evaluation, and quality gates
• Register models with versions, metadata, approvals, dependencies, and promotion criteria
• Deploy batch or online endpoints with environments, scaling, security, traffic, and rollback
• Integrate source control, tests, pipelines, approvals, infrastructure as code, and release stages
• Monitor service health, latency, errors, data drift, model performance, bias, and operational cost
• Define alerts, investigation, human review, retraining, retirement, audit, and incident practices

SkillDynamo Training Approach

SkillDynamo delivers this course through demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution reviews, and a final improvement plan. Learners repeatedly practice planning, implementation, evidence collection, testing, correction, and communication so the skills transfer confidently into real workplace situations.