Microsoft Fabric with MLOPS
Build practical skills in building governed machine-learning lifecycles in Microsoft Fabric from lakehouse data and experiments to pipelines, deployment, monitoring, versioning, responsible AI, and continuous improvement through four structured lessons with demonstrations, hands-on activities, troubleshooting, validation checklists, and an applied improvement plan.
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-by SkillDynamo
- 0 Students
- Last updated
Course Description
This SkillDynamo course develops practical, role-relevant capability in building governed machine-learning lifecycles in Microsoft Fabric from lakehouse data and experiments to pipelines, deployment, monitoring, versioning, responsible AI, and continuous improvement. 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 Fabric workspaces, lakehouses, notebooks, compute, OneLake data, permissions, environments, and data-science assets
- Prepare, profile, clean, transform, split, and document data with quality, lineage, and reproducibility controls
- Engineer features and train suitable models using notebooks, libraries, parameters, scalable processing, and repeatable code
- Track experiments, metrics, artifacts, models, lineage, versions, and approvals with MLflow-compatible practices
- Create training, evaluation, registration, batch-scoring, and orchestration pipelines with reusable components
- Apply source control, tests, deployment pipelines, environments, approvals, rollback, and secure configuration
- Monitor model quality, drift, data, failures, latency, capacity, cost, usage, and business outcomes
- Apply responsible AI, access, privacy, audit, incident, retraining, retirement, support, and lifecycle governance
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.
Curriculum
- 1 Section
- 4 Lessons
- 24 Hours
- Course Curriculum4 Items
- 1.1Microsoft Fabric with MLOPS — Lesson 1: Fabric Data Science and Lakehouse Foundations6 hours
- 1.2Microsoft Fabric with MLOPS — Lesson 2: Experiment Tracking, Feature Engineering and Model Training6 hours
- 1.3Microsoft Fabric with MLOPS — Lesson 3: Pipelines, Deployment and Automated Delivery6 hours
- 1.4Microsoft Fabric with MLOPS — Lesson 4: Monitoring, Responsible AI and MLOps Operations6 hours
SkillDynamo Expert Team
SkillDynamo instructors combine platform knowledge, practical implementation experience, guided demonstrations, scenario-based exercises, troubleshooting methods, and validation checklists to help learners apply the course skills confidently in real work.
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