Phase 10: Java Programming for QA

Go from first principles to running production NLP systems: tokenization, embeddings, transformers, fine-tuning, RAG, prompt engineering, alignment, evaluation, safety, and enterprise-grade serving.

Go from first principles to running a production ML platform: data versioning, feature stores, CI/CD/CT, Kubernetes serving, monitoring, drift detection, security, cost, LLMOps, and incident response.

A 180-hour beginner-to-expert AI engineering program covering software, data, ML, deep learning, vision, NLP, LLMs, RAG, agents, MCP, MLOps, cloud, security, architecture, and AI SaaS.