Academy learning map

Build the core. Then specialize with intent.

KnowledgeOS combines one sequenced, twelve-volume handbook with thirteen focused engineering courses. Use the handbook for durable foundations, then choose the specialization that matches the systems you need to build or lead.

Core phases: 4
Handbook volumes: 12
Focused courses: 13
Available study: 98 hr 45 min

72 hr 50 min in the core handbook plus 25 hr 55 min across the focused catalog. Focused courses are elective paths, not mandatory steps.

How the academy fits together

One academy, two connected layers.

Core curriculum

The twelve-volume handbook supplies the concepts, mathematics, engineering progression, and architecture judgment that later topics depend on.

12 volumes · 161 chapters · 72 hr 50 min

Specialization tracks

Focused courses deepen a role or production system. Choose one after reaching its recommended core phase; complete all only when your role genuinely spans them.

13 courses · 25 hr 55 min

Find your core entry point

Start with evidence, not confidence.

Skip a core phase only when you can explain its outcomes and apply them in a design or implementation review.

New to AI or changing careers

Begin at Phase 1 and keep the sequence intact.

Comfortable with ML and deep learning

Review Phase 1 outcomes, then begin at Volume 5.

Already building RAG or agent systems

Use Phases 2 and 3 to close engineering gaps before production.

Leading platforms, risk, or architecture

Audit Phases 1-3, then use Phase 4 as the decision framework.

Phase 01

Build the foundations

AI history, mathematics, machine learning, and deep learning mental models.

Exit evidence: Explain how modern AI systems learn, where their behavior comes from, and how core model families differ.

Phase 02

Engineer generative systems

LLMs, prompt systems, retrieval pipelines, evaluation, and model serving.

Exit evidence: Build evidence-grounded generative applications and reason about their quality, latency, cost, and failure modes.

Phase 03

Design agentic workflows

Tools, memory, planning, MCP, orchestration, and multi-agent coordination.

Exit evidence: Design bounded agents that use tools safely, recover from failure, and remain observable under production load.

Phase 04

Lead in production

Architecture, reliability, security, cost control, governance, and risk.

Exit evidence: Make architecture and governance decisions that turn capable prototypes into accountable enterprise systems.

Focused specialization

Choose the systems you want to become responsible for.

These courses extend the core curriculum without duplicating its sequence. Follow one track for role depth, or combine tracks when your production ownership crosses boundaries.

Track 01

Build production AI applications

Turn model capabilities into dependable backend, multimodal, and real-time product experiences.

Best after Core Phase 2

Exit evidence: Ship an AI application with explicit model boundaries, grounded context, multimodal or streaming behavior, evaluation, and operational controls.

Track 02

Engineer agents and developer workflows

Progress from bounded agent loops to retrieval, tools, protocols, durable execution, and repository automation.

Best after Core Phase 2; use Core Phase 3 alongside it

Exit evidence: Design an agentic workflow that limits authority, preserves evidence, recovers from failure, and can be evaluated before release.

Track 03

Own models and AI infrastructure

Go beneath application APIs into adaptation, inference serving, GPU economics, and distributed training systems.

Best after Core Phase 2

Exit evidence: Choose and defend a model adaptation, serving, or training architecture using measured quality, throughput, latency, resilience, and cost evidence.

Track 04

Lead enterprise AI platforms

Connect governed data, reliability, security, and architecture decisions into an accountable production platform.

Best after Core Phase 3

Exit evidence: Review an enterprise AI design across data authority, evaluation, security, SLOs, failure recovery, capacity, and organizational ownership.