MLOps Training — ML Lifecycle Automation, Model Deployment & Monitoring

Who Should Attend

This program is for ML engineers, data scientists moving to production, and DevOps engineers supporting ML teams. If your models take months to reach production, there’s no standard way to deploy or monitor them, and model performance degrades without anyone noticing — MLOps teaches you to industrialize the path from notebook to production.

Learning Outcomes

• Build automated ML training pipelines (Kubeflow, MLflow) triggered by code, data, or schedule
• Implement a feature store (Feast) for consistent online and offline feature serving
• Deploy models using canary, shadow, and A/B deployment strategies with automated validation
• Configure model monitoring — data drift, concept drift, prediction quality — with automated retraining triggers
• Manage the model lifecycle — registration, versioning, approval, deployment, retirement

Course Modules

1. MLOps Fundamentals — MLOps vs. DevOps. ML lifecycle. MLOps maturity model. Notebook to production.
2. Experiment Tracking — MLflow Tracking. Experiment organization. Metric comparison. Reproducibility.
3. Automated Training Pipelines — Kubeflow Pipelines, SageMaker Pipelines. Triggered execution. Hyperparameter tuning.
4. Feature Stores — Feast architecture. Online vs. offline serving. Feature lineage. Point-in-time correctness.
5. Model Registry — MLflow Model Registry. Versioning. Stage transitions. Approval workflows. Metadata.
6. Model Deployment Strategies — Real-time (KServe, Seldon). Batch. Canary and shadow deployments. A/B testing.
7. Model Monitoring — Data drift. Concept drift. Prediction quality. Evidently AI, WhyLabs. Automated retraining triggers.
8. ML Infrastructure — GPU scheduling. Kubernetes for ML. Terraform for ML infrastructure. Cost optimization.
9. ML Governance — Model cards. Lineage. Audit trails. Bias monitoring. Explainability (SHAP, LIME).
10. CI/CD for ML — Testing ML pipelines. Data validation in CI. Model validation. Safe deployment.
11. LLM-Specific MLOps — RAG operations. Prompt versioning. LLM evaluation. Guardrails. (Preview of LLMOps course.)
12. Capstone: Production ML System — Build end-to-end MLOps: training pipeline, feature store, registry, deployment, monitoring.

Hands-on Labs (24 total)

Labs include: “Build a Kubeflow pipeline that trains, evaluates, and registers a model,” “Deploy a model with KServe using canary deployment with automated traffic shifting,” “Configure drift monitoring with Evidently AI and trigger automated retraining,” “Implement a Feast feature store for online and offline feature serving.”

Frequently Asked Questions

Is MLOps just DevOps for ML? Mostly — with important additions. MLOps extends DevOps with ML-specific concerns: data versioning alongside code versioning, experiment tracking, model registries (not just container registries), drift monitoring (models degrade differently than traditional services), and the fact that testing ML requires statistical validation, not just pass/fail assertions.

Do I need a GPU for the labs? No. Lab environments with GPU access are provided. You’ll schedule GPU-dependent training jobs on provided Kubernetes clusters. Your laptop only needs a browser and terminal.