Master MLOps and Production ML
A production-grade curriculum for engineers who ship and operate ML systems.
Most ML courses end when the model is trained. This curriculum starts there - and teaches you how to version, test, deploy, monitor, and manage ML in production.
The Curriculum
15 modules. From experiment tracking to cost management - the complete MLOps engineering stack.
MLOps Foundations
Who it's for: Engineers learning the principles and tools of ML operations.
| Module | Topics |
|---|---|
| 01 - MLOps Foundations | What is MLOps, maturity levels, team structures, ML lifecycle |
| 02 - Experiment Tracking | MLflow, Weights & Biases, experiment organization, reproducibility |
| 03 - Data Versioning | DVC, data lineage, dataset snapshots, reproducible training |
| 04 - Model Registry and Lifecycle | Model versioning, stage transitions, approval workflows, metadata |
CI/CD and Infrastructure
Who it's for: Engineers building automated ML pipelines and deployment infrastructure.
| Module | Topics |
|---|---|
| 05 - CI/CD for ML | CI for ML code, testing ML pipelines, automated retraining, deployment gates |
| 06 - Containerization and Packaging | Docker for ML, multi-stage builds, model packaging, reproducible environments |
| 07 - ML Pipeline Orchestration | Orchestration concepts, Kubeflow, Prefect, Airflow, DAG design for ML |
| 08 - Kubernetes for ML | Kubernetes fundamentals, GPU scheduling, model serving on K8s, autoscaling |
Monitoring and Platforms
Who it's for: Engineers responsible for ML reliability, drift detection, and experimentation.
| Module | Topics |
|---|---|
| 09 - Monitoring and Observability | Data drift, model drift, alerting, dashboards, SLOs for ML |
| 10 - Cloud ML Platforms | AWS SageMaker, GCP Vertex AI, Azure ML - architecture, trade-offs, migration |
| 11 - A/B Testing and Experimentation | Statistical foundations, experiment design, multi-armed bandits, feature flags |
Advanced MLOps
Who it's for: Senior engineers managing LLMOps, infrastructure as code, and ML cost optimization.
| Module | Topics |
|---|---|
| 12 - LLMOps Pipelines | Fine-tuning ops, prompt versioning, LLM deployment, evaluation pipelines |
| 13 - Infrastructure as Code for ML | Terraform for ML, Pulumi, GPU provisioning, reproducible infrastructure |
| 14 - Feature Engineering | Feature engineering fundamentals, feature pipelines, transformation patterns |
| 15 - Cost Management for ML | ML unit economics, spot instances, right-sizing, inference optimization, budgeting |
What You Will Be Able to Do
After completing this curriculum:
- Build end-to-end ML pipelines from data ingestion to model serving with full automation
- Deploy models on Kubernetes with GPU scheduling, autoscaling, and canary deployments
- Detect and respond to drift before your models silently degrade in production
- Design experiments with proper statistical rigor - not just A/B tests that look right
- Manage ML infrastructure as code for reproducible, auditable environments
- Optimize ML costs across training, inference, and storage
The Engineering Standard
Every lesson in this curriculum:
- Starts with a real production incident or operational failure
- Covers the architecture and trade-offs, not just the tool commands
- Includes working configurations and production-grade patterns
- Ends with operational scenarios that test your MLOps thinking
This is not a tutorial platform. It is an engineering curriculum.
Career Outcomes
Prepared for roles including:
- MLOps Engineer
- ML Platform Engineer
- ML Infrastructure Engineer
- Production ML Engineer
- AI DevOps Engineer
- Site Reliability Engineer (ML)
Certification (Coming Soon)
EngineersOfAI - MLOps Engineering Certification
Practical. Operations-focused. Production-ready. For engineers who keep ML systems running - not just engineers who train models.