Course Details
MLOps & AI Infrastructure
Learn to deploy, scale, and monitor machine learning models in production using Docker, Kubernetes, and cloud services.
Hands-on Highlights
This program is designed around shipping functional, production-ready AI applications from day one. You will move beyond theory and build real products.
- 1Containerize AI models using Docker
- 2Build CI/CD pipelines for ML models with GitHub Actions
- 3Scale model inference using Kubernetes
- 4Implement model drift monitoring and alerting
Detailed Syllabus
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
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
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
Format & Location
Hybrid Learning
Choose between intensive IRL bootcamps at our physical campuses in Pondicherry, Vellore, or Coimbatore, or join our flexible online programs designed for working professionals.
Prerequisites
Who is this for?
We assume a fundamental understanding of Python and basic programming concepts. This is for developers looking to master deep learning and build real AI products.
Elite Mentorship
Learn from builders
Learn directly from engineers who have built and scaled AI systems. Get code reviews, architecture feedback, and career guidance.
Outcomes
Ship real products
Our primary focus is helping you build a strong portfolio of real, deployed AI products to show employers. All graduates receive a verifiable certificate.
Ready to build?
Join our next cohort to get started.