Project Ladder¶
Projects are the spine of the roadmap. Each one should be small enough to finish, but real enough to show what you learned.
| # | Stage | Project | What it proves |
|---|---|---|---|
| 0 | Orientation | A learning log, environment checklist, use-case decision memo, and first roadmap plan. | Build the map before choosing tools. |
| 1 | Foundations | A tested Python data application with a CLI or API, setup notes, and a short data report. | Become fluent with code, data, math, and systems basics. |
| 2 | Machine Learning | An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card. | Learn how models learn from data and how evaluation can lie. |
| 3 | Deep Learning | A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes. | Train, debug, and reason about neural networks. |
| 4 | LLMs | An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases. | Understand tokens, transformers, generation, adaptation, and evaluation. |
| 5 | AI Applications | An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis. | Build reliable AI products around models. |
| 6 | AI Agents | A tool-using agent with typed tools, memory, traces, task evals, prompt-injection tests, and an architecture README. | Build controlled tool-using systems, not vague autonomy. |
| 7 | Model Infrastructure | A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards. | Operate AI systems as production software. |
| 8 | Optimization and Hardware | An inference benchmark and optimization report for an open-weight or hosted model workload. | Make inference faster, cheaper, and more predictable. |
| 9 | Security, Blockchain, ZKML | A threat model, red-team report, smart contract security lab, and tiny ZKML or verifiable computation demo. | Secure, govern, and verify AI-enabled systems. |
| 10 | Mastery | A capstone AI system with architecture, implementation, evaluation, deployment, observability, cost, security review, and portfolio narrative. | Own a complete AI system end to end. |