KnowledgeOS · AI Engineering Academy
Learn AI engineering from first principles to production.
Structured courses for software engineers who want to build, evaluate, and lead enterprise AI systems with real architectural judgment.
- learning options
- 14
- handbook volumes
- 12
- handbook chapters
- 161
- available study
- 98 hr 45 min
Course Catalog
One core path. 13 focused ways to go deeper.
Begin with the complete curriculum, or choose a focused course for the engineering problem directly in front of you.
Flagship Curriculum
The Complete GenAI Engineering Handbook
A twelve-volume curriculum spanning AI foundations, mathematics, machine learning, LLMs, RAG, agents, enterprise architecture, and responsible AI.
Beginner to Architect
12 volumes · 161 chapters
72 hr 50 min lesson reading
- Foundations
- LLMs
- RAG
- Agents
- Enterprise AI
Software Engineering Track
AI Coding Agents for Professional Engineers
Take coding-agent work from a bounded repository issue to an evidenced patch, then design the security, evaluation, CI/CD, recovery, and operating controls required at enterprise scale.
Beginner Engineer to Architect
35 sections · 4 parts + capstone
About 90 min · 3 labs
- Coding Agents
- Repository Context
- Verification
- Secure Automation
Backend Engineering Track
Production GenAI with Java and Spring AI
Turn Spring engineering skills into production AI systems with typed model boundaries, authorized RAG, guarded tools, MCP, observability, evaluation, resilience, security, and release evidence.
Java Engineer to AI Architect
44 sections · 4 parts + engineering studio
About 120 min · 3 labs
- Java
- Spring AI 2.0
- Enterprise RAG
- Production Engineering
Applied AI Systems Track
Multimodal AI Engineering
Engineer production systems across images, documents, audio, and video with evidence provenance, multimodal RAG, fusion, accessibility, evaluation, security, cost control, and incident-ready operations.
Beginner Engineer to AI Architect
44 sections · 4 parts + engineering studio
About 125 min · 3 labs
- Vision
- Audio and Video
- Multimodal RAG
- Production AI
AI Security Track
AI Security and Red Teaming for Production Systems
Threat-model and defend production AI across prompts, retrieval, tools, memory, models, supply chains, detection, red-team evidence, containment, and incident recovery.
Beginner Engineer to AI Security Architect
44 sections · 4 parts + security studio
About 130 min · 3 labs
- Threat Modeling
- Prompt Injection
- AI Red Teaming
- Incident Response
Model Engineering Track
Fine-Tuning, Alignment and Model Adaptation
Diagnose when weights should change, then engineer governed data, SFT, LoRA and QLoRA, preference optimization, distillation, evaluation, adapter serving, and incident-ready operations.
Beginner Engineer to Model Adaptation Architect
46 sections · 5 parts + adaptation studio
About 135 min · 3 labs
- Fine-Tuning
- LoRA and QLoRA
- Preference Optimization
- Adapter Serving
Architecture Track
GenAI System Design and Architecture Interviews
Turn an AI product idea into a quantified production architecture covering capacity, latency, RAG, model routing, tenancy, reliability, evaluation, security, regional recovery, and design interviews.
Beginner Engineer to Architect
38 sections · 4 parts + capstone
About 105 min · 3 labs
- System Design
- Capacity
- Reliability
- Architecture Interviews
Production Engineering Track
LLMOps: Evaluation, Observability and Production Reliability
Turn probabilistic AI behavior into release evidence with representative datasets, calibrated graders, privacy-aware traces, semantic SLOs, controlled canaries, rollback, and incident operations.
Beginner Engineer to Architect
45 sections · 4 parts + reliability studio
About 120 min · 3 labs
- LLMOps
- Evaluation
- Observability
- Production Reliability
AI Infrastructure Track
LLM Inference, GPU Serving and Performance Engineering
Turn model artifacts into dependable services through prefill and decode mechanics, KV cache, continuous batching, quantization, GPU topology, distributed serving, SLOs, capacity, cost, and release safety.
Backend Engineer to AI Infrastructure Architect
52 sections · 5 parts + inference studio
About 150 min · 3 labs
- LLM Inference
- GPU Serving
- Performance Engineering
- Distributed Systems
AI Data Infrastructure Track
AI Data Platform Engineering for Generative AI
Build the governed evidence plane behind RAG, agents, evaluation, and model adaptation through source authority, CDC, document processing, lineage, access control, indexes, deletion, reliability, and cost.
Data Engineer to AI Platform Architect
48 sections · 5 parts + data platform studio
About 145 min · 3 labs
- AI Data Platforms
- Streaming and CDC
- Knowledge Pipelines
- Data Governance
ML Systems Track
Distributed AI Training and ML Systems Engineering
Scale a verified training loop through DDP, FSDP, ZeRO, model parallelism, high-throughput data, atomic checkpoints, GPU scheduling, observability, recovery, and outcome-based economics.
ML Engineer to Distributed AI Systems Architect
52 sections · 5 parts + training systems studio
About 155 min · 3 labs
- Distributed Training
- GPU Clusters
- Parallelism
- ML Systems
Conversational Systems Track
Real-Time Voice AI and Conversational Agent Engineering
Engineer low-latency voice sessions across WebRTC, SIP, streaming speech, turn-taking, interruption, tools, RAG, handoff, evaluation, consent, safety, scaling, and incident operations.
Backend Engineer to Voice AI Architect
48 sections · 5 parts + voice systems studio
About 145 min · 3 labs
- Voice AI
- WebRTC and SIP
- Conversational Agents
- Real-Time Systems
Complete Field Manual
AI Agents, Agentic AI & RAG
A continuous path from agent fundamentals to governed RAG, retrieval evaluation, MCP, multi-agent design, and three executable architecture labs.
Intermediate
47 sections · 4 parts + capstone
About 100 min · 3 labs
- Agents
- Agentic RAG
- Retrieval
- MCP
Hands-on Companion
Agentic AI: The Practitioner’s Companion
Move from concepts to shipping with context budgeting, tool security, durable execution, framework selection, evaluation gates, and three executable labs.
Practitioner
25 sections · 4 parts + studio
About 35 min · 3 labs
- Context Engineering
- Agent Loops
- Tooling
- Security
Learning Path
Progress without losing the system view.
Four core phases move from mental models to accountable architecture. Focused tracks then deepen the application, agent, infrastructure, or platform systems your role requires.
See the academy learning path- 01
Build the foundations
AI history, mathematics, machine learning, and deep learning mental models.
- 02
Engineer generative systems
LLMs, prompt systems, retrieval pipelines, evaluation, and model serving.
- 03
Design agentic workflows
Tools, memory, planning, MCP, orchestration, and multi-agent coordination.
- 04
Lead in production
Architecture, reliability, security, cost control, governance, and risk.
Engineering, Not Hype
Learn the decisions behind reliable AI systems.
Mental models first
Understand models, retrieval, agents, and evaluation as connected systems rather than isolated APIs.
Build to understand
Move from executable examples and labs to production failure modes, observability, and operational trade-offs.
Architect for reality
Design authority boundaries, security controls, cost limits, and governance before systems reach production.
Start with the full map
Build a durable AI engineering career, one decision at a time.
Compare the twelve-volume core with all focused specialization tracks, then choose the entry point and depth that match your current evidence and responsibilities.