AI Engineering & Innovation
Advanced ML systems, transformer engineering, production GenAI and MLOps — for engineers building the next AI product, not just a model.
90 hours · ₹29,999.00
Log in to enrolModule-by-module curriculum
M1 — Advanced ML Systems Design 15 hrs
- ML system design patterns at scale
- Feature engineering for production pipelines
- Model selection trade-offs
- Experiment tracking with MLflow / Weights & Biases
M2 — Deep Learning & Transformer Architectures 15 hrs
- CNN/RNN recap and where they still win
- Attention mechanism, explained from first principles
- Transformer architecture deep dive
- Vision transformers
- Fine-tuning pretrained models
M3 — LLM & GenAI Engineering 15 hrs
- LLM internals and inference behavior
- Prompt engineering at production scale
- RAG architecture and vector databases
- Agentic workflows and tool-use
- Fine-tuning vs. RAG vs. prompting: choosing the right trade-off
- Evaluating LLM output quality systematically
M4 — MLOps & Scalable Deployment 15 hrs
- CI/CD pipelines for ML
- Containerization and orchestration basics (Docker/Kubernetes)
- Model serving at scale
- Monitoring, observability and drift detection
- A/B testing and cost optimization
M5 — Responsible & Secure AI Engineering 15 hrs
- Adversarial robustness and attack surfaces
- Model security fundamentals
- Bias and fairness testing at scale
- Explainability with SHAP and LIME
- Compliance and audit trails
- Red-teaming generative AI systems
M6 — Capstone — Innovation Sprint 15 hrs
- Identifying a genuinely novel AI application
- Rapid prototyping under real constraints
- Technical architecture and build
- Demo Day storytelling and pitching
Capstone project
Innovation Sprint Product
Design, build and deploy an original AI product addressing a real innovation brief, and prepare a Demo Day pitch. Upload your source code (zip or repo link), deployment link/screenshots, and pitch deck.