ShipAI
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Learn AIFull curriculum outline

Concepts

Foundations → deeper theory

Core Concepts
  • What is AI (and what it is not)
  • Classical ML literacy
  • Tokenization
  • Self-attention
  • Embeddings and similarity
  • Prompt engineering fundamentals
  • Structured outputs
  • RAG building blocks
  • Agents and the ReAct loop
  • Evals fundamentals
  • Serving and streaming
  • Networking for AI apps
  • LLM project lifecycle
  • Multimodal basics
  • Privacy and data for AI apps
  • Open-weight models vs closed APIs
Advanced Concepts
  • Scaling laws and compute
  • Fine-tuning with LoRA and QLoRA
  • Alignment basics — RLHF and DPO
  • Mixture of Experts (MoE)
  • Context engineering
  • Reasoning and test-time compute
  • Multi-agent orchestration
  • Guardrails and safety systems

Tools

Everyday stacks → production tooling

Key Tech
  • LangGraph and LangChain patterns
  • Hugging Face
  • Ollama
  • Model Context Protocol (MCP)
  • LlamaIndex
  • OpenAI and Anthropic APIs
  • Chroma
  • Weights & Biases
Advanced Key Tech
  • Ray
  • Triton Inference Server
  • Kafka for evented AI
  • OpenTelemetry for LLMs
  • TensorRT-LLM and SGLang
  • MLflow for LLMOps

Data & inference

Retrieval data plane + serving

Data & Databases for AI
  • Vector databases — what, why, and how
  • Choosing vector stores — Pinecone, Weaviate, Chroma, pgvector
  • Postgres and pgvector
  • Redis for AI caching
  • Chunking and metadata
  • Hybrid search and rerankers
Inference
  • vLLM
  • KV-cache, prefill, and decode
  • Continuous batching
  • Quantization for inference
  • Speculative decoding
  • Cost and latency routing

Guided path

Projects · foundations → build → agents → production

Foundations

Onboarding
  • How this course works
  • Python, tooling, and compute options
  • API keys, budgets, and responsible use
  • Rubric: tests, eval JSON, and README
What happened in AI (till now)
  • Symbolic AI → why rules hit a wall
  • Classical ML era
  • Deep learning boom
  • Attention → Transformer → GPT lineage
  • Generative AI landscape
  • Open-weight vs closed API
  • Jobs: AI eng, ML eng, research, FDE, PM
ML/DL literacy
  • Data splits and leakage
  • Loss, gradients, and overfitting
  • Embeddings before LLMs
  • Autograd intuition
  • PyTorch training loop
Build an LLM from scratch
  • Tokenization and BPE
  • Next-token prediction
  • MLP language model
  • Self-attention
  • Tiny GPT / mini-LLM end-to-end
  • Sampling (temperature, top-k/p)
  • Pretrain vs SFT vs preference (map only)
  • Why your tiny LLM ≠ ChatGPT

Generative AI practice

Talk to models in the real world
  • Chat APIs and message roles
  • Structured output
  • Prompt versioning
  • Multimodal in → text
  • Code assist loops
RAG
  • Embeddings and similarity
  • Chunking
  • Vector DB
  • Hybrid search
  • Rerank / rewrite
  • Citations and failures
Build & serve your SLM
  • When SLM/FT beats RAG or prompting
  • Dataset curation and licenses
  • LoRA / QLoRA fine-tune
  • Eval vs base / teacher
  • Serve your SLM
  • Quantization tradeoffs

Agents & agentic systems

Build real AI agents
  • LLM vs agent; loops; stop conditions
  • Tools (schema, validation, side effects)
  • Planning vs reactive
  • Memory
  • Human-in-the-loop
  • Trace and debug trajectories
  • Framework packaging
Skills, MCP, context engineering
  • Skill vs tool vs prompt
  • Author skills
  • Dynamic skill loading
  • MCP servers and clients
  • Context budgets
  • Coding-agent skill patterns
Agentic workflows & multi-agent
  • Workflow patterns
  • Shared state and handoffs
  • Agentic RAG
  • Long-running / checkpoint / resume
  • Failure isolation
  • Harness thinking

Production + how companies actually do it

Evals, guardrails, safety
  • Offline vs online evals
  • LLM-as-judge
  • Component vs E2E
  • Injection / jailbreaks
  • Guardrails
  • Canaries
Deploy, cost, latency, observability
  • API + streaming
  • Cost accounting
  • Caching and latency
  • Tracing tool/LLM spans
  • Retries, rate limits, idempotency
  • Runbook
How real companies use AI
  • Netflix — in-house LLM serving
  • Uber — agent platform, MCP, gateway
  • Meta — LLM-scale training literacy
  • Stripe — coding agents / harness
  • Shopify — production merchant agent
  • Airbnb — verify-heavy workflows
  • NVIDIA — agent toolkits and eval/obs
  • Google — agentic training / RL direction
AI system design
  • Build vs buy
  • UX of uncertainty, citations, feedback
  • Data flywheels
  • Privacy / retention sketch
  • Capstone proposal

Frontier + capstone

What’s coming + staying current
  • Reasoning models / test-time compute
  • Multimodal agents
  • On-device / small models
  • Synthetic data loops
  • Safety, regulation, dual-use
  • Personal radar
Capstone
  • Capstone requirements and pick-two
  • Architecture and eval plan
  • Build and harden
  • Write-up and demo
Sign up / in
FoundationsOnboarding

Foundations

Onboarding

Set up the portfolio monorepo, tooling, API hygiene, and the tests/eval/README rubric so the lab is ready.

Milestone Lab ready.

Lessons in this module

  • 1. 40 minHow this course worksProject: Init course-portfolioOpen
  • 2. 55 minPython, tooling, and compute optionsProject: make doctorOpen
  • 3. 45 minAPI keys, budgets, and responsible useProject: First logged LLM callOpen
  • 4. 50 minRubric: tests, eval JSON, and READMEProject: First golden fixtureOpen
NextHow this course works

ShipAI curriculum

A structured path from foundations to production agents — lessons, projects, and milestones in one course. Free forever.

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On this page

  • Overview
  • Lessons
  • How this course works
  • Python, tooling, and compute options
  • API keys, budgets, and responsible use
  • Rubric: tests, eval JSON, and README