The AI Atlas

Comprehensive resources for AI engineering, product, and strategy.
verified Co-Created by Kiran Shirol and Claude
32 Courses · Self-paced learning tracks
Track 1

Strategy & Product

For leaders and PMs building AI-first organizations.
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AI for Executives
Strategic frameworks, ROI, and navigating the AI landscape for business leaders.
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AI Product Management
From ideation to launch: building products powered by machine learning and LLMs.
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AI Agents for the Enterprise
Deploying AI agents into real business workflows — integration, adoption, compliance, and why 60% of pilots fail.
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Track 2

Foundations

Essential math, theory, classic ML, and deep learning fundamentals.
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AI Fundamentals
The history, core concepts, and neural network basics you need to know.
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Math for AI
Linear algebra, calculus, and probability demystified for AI practitioners.
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Classic Machine Learning
Regression, classification, random forests, and foundational ML algorithms.
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Deep Learning Fundamentals
12 chapters: neural nets, CNNs, RNNs, attention, Transformers, GANs, VAEs, and the path to LLMs.
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Natural Language Processing
From text preprocessing to BERT — tokenization, classification, NER, sequence labeling, and the evolution from rules to transformers.
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Track 3

LLM Mastery

Large Language Models, prompting, fine-tuning, RAG, and advanced reasoning.
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How LLMs Work
Transformers, attention mechanisms, and what happens under the hood.
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Prompt Engineering
Advanced techniques to reliably steer and control language models.
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Fine-Tuning
LoRA, QLoRA, RLHF, and adapting foundation models to your specific data.
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RAG (Retrieval-Augmented Gen)
Vector databases, embeddings, and giving LLMs access to external knowledge.
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Reasoning & Chain-of-Thought Models
CoT, tree search, test-time compute, verification, tools, benchmarks, and the future of AI reasoning.
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Track 4

Agents & Systems

Building autonomous AI systems and connecting models to tools.
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Agentic AI
Planning, memory, tool use, and the shift from chatbots to autonomous agents.
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OpenClaw
Mastering the open-source framework for building robust AI agents.
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Model Context Protocol (MCP)
The universal standard for connecting AI models to data sources and tools.
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Multi-Agent Systems
Coordination, communication protocols, LLM-based agent frameworks, and production patterns.
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AI-Assisted Coding
Code completion to autonomous agents — context engineering, vibe coding, and AI workflows.
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Context Engineering
Managing what an LLM sees — progressive disclosure, compression, routing, retrieval, and token budgeting.
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Harness Engineering
Constraints, review pipelines, memory, and orchestration — making AI coding agents reliable at scale.
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Autonomous Software Pipelines
Background agents, AI-powered CI/CD, automated migrations, production orchestration, and the self-driving software factory.
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Open Source AI Ecosystem
Hugging Face, Ollama, vLLM, llama.cpp, LoRA — the complete open-source AI stack from local inference to production serving.
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Track 5

Engineering & Ops

Infrastructure, security, and deploying models to production.
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AI Infrastructure
GPUs, distributed training, serving engines, and the hardware behind AI.
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Small Models & Local AI
Quantization, edge deployment, and running powerful models on your laptop.
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Edge AI & TinyML
MCU-ready deployment, LiteRT Micro, RTOS integration, accelerators, benchmarking, and OTA lifecycle discipline.
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AI Security
Prompt injection, data poisoning, red teaming, and securing AI systems.
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AI Economics & Cost Engineering
Token economics, GPU pricing, optimization techniques, agent cost control, and AI FinOps — what AI actually costs and how to spend wisely.
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Anatomy of an LLM File
Tensor structures, embedding matrices, attention weights, tokenizer internals, KV cache, and how they map to the transformer architecture.
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Track 6

Evaluation & Multimodal

Measuring AI quality and building systems that see, hear, and generate.
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LLM Evaluation & Observability
Benchmarks, LLM-as-Judge, RAG eval, guardrails, and production observability for AI systems.
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Multimodal AI & Generative Media
Vision models, diffusion, text-to-image/video, speech AI, VLMs, and multimodal agents.
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Reading Model Cards
YAML metadata, benchmarks, licenses, file formats, and the signals that tell you whether a model fits your task.
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Track 7

MLOps & Responsible AI

Production ML and LLM pipelines, governance, and building AI responsibly.
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MLOps & LLMOps
Experiment tracking, registries, CI/CD, serving, LLM gateways, prompts, monitoring, and the full stack.
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AI Ethics & Responsible AI
Bias, fairness, explainability, privacy, LLM ethics, governance, regulation, and building ethical teams.
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