IRL & Online Program
MLOps & AI Infrastructure
Learn to deploy, scale, and monitor machine learning models in production using Docker, Kubernetes, and cloud services.
Hands-on Highlights
- Containerize AI models using Docker
- Build CI/CD pipelines for ML models with GitHub Actions
- Scale model inference using Kubernetes
- Implement model drift monitoring and alerting
Detailed Syllabus
Week 1-2
Containerization & CI/CD
- Writing robust Dockerfiles for Python ML environments
- Docker Compose for multi-container apps
- Automating model testing
- Building CI/CD pipelines with GitHub Actions
Week 3-4
Model Deployment & Serving
- REST vs gRPC for model serving
- Deploying APIs with FastAPI and Uvicorn
- Using Triton Inference Server/TorchServe
- A/B testing and canary deployments
Week 5-6
Kubernetes & Monitoring
- Introduction to Kubernetes (Pods, Services, Deployments)
- Scaling inference workloads on K8s
- Monitoring models with Prometheus & Grafana
- Detecting data drift and setting up alerts
Target Roles & Career Paths
MLOps Engineer
AI Infrastructure Engineer
Platform Engineer (ML)
Backend Engineer (AI)
These are the primary roles you will be equipped to apply for upon successful completion of the course and portfolio projects.