Build the Skills That Power the AI Economy

Master the full MLOps lifecycle — from feature engineering and model training to CI/CD pipelines, model registries, and production monitoring. Covers LLMOps patterns for large language model deployment at scale.

• Design and implement end-to-end ML pipelines
• Deploy models with vLLM, Triton, and SageMaker
• Build observability stacks for production AI
• Implement LLM fine-tuning with QLoRA and PEFT

Go deep on LLMs, RAG architectures, agentic systems, and multimodal AI. Build production-grade generative AI applications using the latest open-source and frontier models.

• Build RAG pipelines with vector databases
• Design multi-agent systems with LangGraph
• Fine-tune and align open-source LLMs
• Deploy generative AI APIs on AWS and GCP

Train for the most in-demand AI role in enterprise: the engineer who sits between the model and the customer. Learn to scope, build, and ship AI solutions directly in client environments.

• Scope and deliver AI projects end-to-end
• Integrate AI into existing enterprise systems
• Communicate technical decisions to stakeholders
• Operate autonomously in client-facing environments