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AI Engineering course · one course, beginner to advanced
Evaluating LLM systems, operating them in production, and securing them: error analysis, judges and eval statistics, fine-tuning vs RAG, inference and guardrails, then prompt injection and agent safety.
All 206 lessons plus every future lesson, with runnable code and enterprise diagrams. One payment, lifetime access.
Operating large language models in production: reading a model name, fine-tuning vs RAG, parameter-efficient tuning, evaluation, prompt management, vector databases, inference optimization, guardrails, and agents.
The discipline the industry pays most for and teaches least: error analysis, golden datasets, LLM-as-judge and its failure modes, validating a judge against human labels, evaluating RAG and agents, offline versus online, and benchmark CI. Methodology, not tool tours.
The security discipline for LLM and agent systems: prompt injection and the lethal trifecta, practical defenses, tool sandboxing and least privilege, agent identity and delegated authorization, and the OWASP Agentic threat model with red-teaming. Threat models and controls, not fear.
206 lessons in total, across 3 levels
Where the course starts: how production ML works (packaging, serving, pipelines, feature stores, monitoring), then how language models read text, with tokens, tokenizers, embeddings and search by meaning. Start here.
74 lessons7 chapters
Data engineering for ML, retrieval and RAG in production, and running agents in production: pipelines, ingestion, versioning and data contracts, then chunking, retrieval and grounding, then tool calls, planning, memory and failure handling.
70 lessons6 chapters