AI Academy — one curriculum, four lines

Understand how language models work, build AI agents on top of them, govern the systems you ship, and get certified on the cloud underneath: 265 hand-built modules, 48 interactive tools, and a 1904-term glossary across four connected academies. Free, static, no account needed.

LLM Academy

Transformers from the inside, training, inference and serving, RAG and fine-tuning, evaluation, security — and what each concept means on AWS, Azure, and Google Cloud.

Foundations — What a language model is, what next-token prediction really means, and what LLMs can honestly do
Inside the Transformer — Tokens, embeddings, attention, and the block — the machine, opened up
Pre-training — Data pipelines, objectives, scaling laws, and the engineering of a training run
Post-training & Alignment — SFT, RLHF, DPO, and reasoning training — how a base model becomes an assistant
Inference & Serving — Sampling, the KV cache, quantization, batching — where latency and cost are made
Adapting LLMs — Prompting, RAG, fine-tuning, distillation — and how to choose between them
Evaluation — Benchmarks, contamination, judges, and eval harnesses you can defend
Security & Risk — Hallucination, injection, leakage, red teaming — the model-level risk map
LLMs on the Cloud — Bedrock, Azure AI Foundry, Vertex AI, GPUs, cost models, reference architectures
Model Landscape — Model families, open vs closed weights, multimodal and small models, model cards

70 modules · 10 tools · 202 glossary terms

AI Agents Academy

Interactive beginner-to-expert AI agents curriculum: architectures and orchestration, MCP and agent protocols, AWS AgentCore, Microsoft Foundry, Vertex AI, security and guardrails, evals, and AgentOps.

Agent Foundations — What an agent actually is, how the loop works, and when not to build one
Architectures & Orchestration — Single agents, multi-agent systems, planning patterns, and the human in the loop
Protocols & Interoperability — Tool calling, structured outputs, MCP, and how agents talk to each other
Building on the Clouds — AWS AgentCore, Microsoft Foundry, and Vertex AI — deploy, secure, and pay for agents
Security & Guardrails — Prompt injection, least privilege, sandboxing, and the guardrail catalog
Evaluation & Observability — Traces, evals, LLM judges, and knowing whether your agent actually works
AgentOps & Production — Versioning, rollout, incidents, isolation — running agents for real

55 modules · 13 tools · 2 exam simulators · 267 glossary terms

AI Governance Academy

Interactive beginner-to-expert AI governance curriculum: the EU AI Act article by article, ISO/IEC 42001, NIST AI RMF, global law, AIGP exam prep, simulators, and downloadable control mappings.

Foundations — What AI is, why it needs governing, and the language of the field
The EU AI Act — The world’s first comprehensive AI law, article by article
ISO Standards — ISO/IEC 42001 and the standards stack, from terminology to certification
NIST & US Law — The AI Risk Management Framework and America’s patchwork of AI rules
Global Landscape — How China, the UN, the OECD, and the rest of the world govern AI
Governance in Practice — Building and running a real AI governance program
Special Topics — Privacy, IP, biometrics, compute, sectors — the frontier and the edges
AIGP Certification — Exam-weighted prep for the IAPP AI Governance Professional credential

60 modules · 17 tools · 2 exam simulators · 604 glossary terms

Certification Academy

Animated, interactive prep for AWS certifications — ML Engineer – Associate (MLA-C02), Generative AI Developer – Professional (AIP-C01), Security – Specialty (SCS-C03) and Data Engineer – Associate (DEA-C01): every architecture a step-through storyboard, every mechanism a hands-on simulation, a quiz after every module, blueprint-weighted drills, and full scaled-score mock exams.

Foundations — The exam, the ML lifecycle on AWS, and the services every question assumes
Data Preparation — Collect, transform, and trust the data — 28% of the exam
Model Development — Choose, train, customize, and evaluate — classic ML and foundation models
Deployment & Orchestration — Endpoints, agents, pipelines, and infrastructure as code
Operations & Security — Monitor, optimize, and secure what you shipped
FM Integration & Data — Design, models, vectors, retrieval, prompts — 31% of the exam
Implementation & Integration — Agents, deployment, enterprise wiring, streaming APIs — 26%
Safety & Governance — Guardrails, data security, responsible AI — 20%
Efficiency & Optimization — Tokens, latency, caching, GenAI observability — 12%
Testing & Troubleshooting — Evaluations, quality gates, diagnosis — 11% and the capstone
Security Foundations — The exam, the four question formats, shared responsibility, and the service map
Detection — See everything: GuardDuty, Security Hub, CloudTrail, logging architecture — 16%
Incident Response — Plan it, automate it, contain it, prove the root cause — 14%
Infrastructure Security — Edge, compute, and network controls — WAF, Inspector, firewalls — 18%
Identity & Access Management — Authentication, temporary credentials, and policy evaluation — the heaviest at 20%
Data Protection — KMS and envelope encryption, TLS everywhere, immutability, secrets — 18%
Foundations & Governance — Organizations, Control Tower, SCPs and RCPs, Config, audit evidence — 14%
Data Engineering Foundations — The blueprint, the service map, and the pipeline vocabulary the exam assumes — on-ramp
Ingestion & Transformation — Streams and batches in, Glue and Spark and EMR, orchestration, and the code you must read — 34%
Data Store Management — Choosing the store, cataloging it, moving it through its lifecycle, and evolving its schema — 26%
Operations & Support — Automating the line, querying it, watching it, and proving the data is fit to use — 22%
Security & Governance — Who you are, what you may touch, encryption and masking, audit logs, and privacy duties — 18%

80 modules · 0 tools · 4 exam simulators · 831 glossary terms

The Library