Certified MLOps Professional — ML Lifecycle Automation, Deployment & Monitoring

Certification Overview

The Certified MLOps Professional credential validates your ability to industrialize the ML lifecycle — automated training pipelines, feature stores, model registries, deployment strategies, and drift monitoring. This certification demonstrates you can take models from notebook to production reliably and at scale.

Who Should Enroll

• ML engineers deploying models to production
• Data scientists wanting production ML skills
• DevOps engineers supporting ML platforms
• Platform engineers building ML infrastructure

Skills Validated

• Training Pipelines: Kubeflow, MLflow, SageMaker Pipelines — triggered, parameterized, automated
• Feature Stores: Feast — online/offline serving, feature lineage, point-in-time correctness
• Model Registry: Versioning, stage transitions, approval workflows, metadata management
• Deployment Strategies: KServe, Seldon — canary, shadow, A/B with automated validation
• Drift Monitoring: Data drift, concept drift, prediction quality, automated retraining triggers
• ML Governance: Model cards, lineage, audit trails, bias monitoring, explainability

Assessment Structure

Practical Lab (build a Kubeflow pipeline, deploy with canary strategy), Capstone Project (end-to-end MLOps platform), Scenario-Based Evaluation.

Career Outcomes

Roles: MLOps Engineer, ML Platform Engineer, Senior ML Engineer, Machine Learning Infrastructure Engineer.