AI Engineering Bootcamp
Learn to build practical ML systems, LLM applications, retrieval pipelines, and agent workflows through a self-paced tutorial curriculum.
- Source repo
- curiousily/AI-Bootcamp
- Stars
- ★ 955
- Last updated
- 12d ago
- License
- MIT
- Primary language
- Jupyter Notebook
- FA score
- 38/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free, no paid service needed
- Setup effort
- High · needs real infrastructure
- You'll need
- Typical use
- Learners moving from Python and PyTorch fundamentals toward production ML workflows can follow the Foundations and MLOps lessons.
- Not a fit if
- Teams seeking one ready-to-deploy agent product
- Learners who cannot configure tools across tutorial projects
- Source review
- 38/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
This self-paced repository is a curriculum for AI engineering, progressing from Python, mathematics, linear models, and PyTorch to MLOps and large language model applications. Its AI systems lessons cover Ollama, local models, prompt engineering, LangChain, DSPy, model evaluation, and fine-tuning. The RAG and context engineering section covers document processing, chunking, vector search, hybrid retrieval, and implementations using LangChain and Ollama. Agent lessons use tools including LangGraph, LangChain, MCP, and Streamlit to build workflows, database assistants, and a local file knowledge assistant. The repository links tutorials and videos, but does not describe a single installer, unified runnable product, or shared deployment boundary; each lesson requires its own setup.
The tutorials guide learners through processing documents and building knowledge bases, then using embeddings, vector databases, BM25, reranking, and query reformulation for retrieval-augmented generation. They also demonstrate running local models with Ollama, building applications with LangChain, and orchestrating workflows or agents with LangGraph. Example projects include chatbots, RAG systems, database question-answering, an MCP file assistant, Reddit analysis reports, and a multi-agent financial analyst. Other lessons cover QLoRA fine-tuning and prompt optimization with DSPy. The repository does not define one command or API for running all examples.
- Learners moving from Python and PyTorch fundamentals toward production ML workflows can follow the Foundations and MLOps lessons.
- Engineers exploring local models, prompting, tool use, or structured output can work through the Ollama Quickstart and AI Engineer Toolkit.
- Developers building question-answering over PDFs, tables, or private documents can use the document processing, chunking, vector database, and RAG tutorials.
- Developers learning LangGraph workflows and agents can follow projects for support tickets, database questions, or a local MCP file assistant.
- Practitioners exploring automated prompt optimization or task-specific model training can study the DSPy and QLoRA lessons.
How do you install or deploy this agent?
This is a tutorial collection. The README provides no repository-wide install command, unified dependency list, environment variables, or first-run steps for a single application. Individual lessons introduce tools such as Python, Ollama, LangChain, LangGraph, and Streamlit; prepare the environment required by the lesson you choose. Some lessons have a Google Colab launch link, but the repository does not say that every lesson runs in Colab.
How do you use this agent?
Choose a lesson from the README curriculum and follow its Read or video link. For example, the Ollama Quickstart covers running local models, the RAG lesson builds knowledge-base question-answering, and the LangGraph introduction builds a support ticket system. The README does not provide a shared launch command or API invocation for these projects, so it does not support a copyable universal first-run command.
What are this agent's strengths and limitations?
- The curriculum spans ML foundations, MLOps, LLM applications, RAG, agents, and fine-tuning, allowing topic-by-topic study.
- Several lessons explicitly use Ollama and local models, including local RAG, workflow, and file assistant examples.
- Lessons link to written tutorials and many include videos; some also offer a Colab launch link.
- It is a tutorial collection rather than one installable product; the README gives no shared dependency list, launch path, or deployment configuration.
- Examples use different frameworks and infrastructure, so learners must configure Python, Ollama, LangChain, LangGraph, or other tools as each lesson requires.
- The README does not specify course costs, support policy, platform compatibility, or whether every example runs offline or in the same environment.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AI Engineering Bootcamp This agent | 38 · Major gaps | FrameworkFree | ★ 955 | 12d ago | Jupyter Notebook | — |
| EgoAlpha Prompt In-Context Learning Guide | 23 · Major gaps | Library / SDKFree | ★ 2.2k | 4mo ago | Jupyter Notebook | — |
| Controllable Complex RAG | 40 · Major gaps | Library / SDKFree + model costs | ★ 1.6k | 1mo ago | Jupyter Notebook | OpenAI API |
| AIDE ML | 48 · Major gaps | CLIFree + model costs | ★ 1.6k | 6d ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
The README mentions local models and a local file agent, showing some attention to limiting data exposure. It provides no permission boundaries, confirmation steps, sensitive-data handling, external-effect controls, or rollback guidance, so those areas receive thin scores or zero. Linked tutorials and a named copyright holder support source attribution (2). A dependency manifest is present, but no security policy or lockfile is shown, so dependency security scores 1.
The README's course sections, tables, and descriptions are generally consistent, and it explicitly distinguishes workflows from agents, supporting a score of 2 for self-consistency. pyproject.toml lists dependencies, but requires Python >=3.13.9 and there are no installation instructions or lockfile; dependency availability is only thinly supported (1). No runtime code or error-handling material is provided, so failure messages score 0.
The curriculum targets AI engineering learners and covers foundations, production systems, RAG, and agents, supporting a score of 2 for audience and scenarios. The README distinguishes predictable workflows from LLM-driven agents, but does not systematically describe capability boundaries or trigger conditions; those score 1 and 0 respectively. Ollama, local models, and Colab are mentioned, but there is no environment setup or compatibility guidance, so environment fit scores 1.
The README organizes lessons by topic and provides tutorial and video entry points, supporting scores of 2 for information architecture and examples. It has no installation notes or FAQ. Project and course names are mostly stable, scoring 1. Limitations are not documented, scoring 0. The LICENSE includes the MIT terms and copyright notice, scoring 3. pyproject.toml declares version 1.0.0, but no changelog or clear maintenance responsibility is shown, so those criteria score 1.
The course index provides direct tutorial and video links and a learning path from fundamentals to agent building, supporting scores of 2 for output usability and marginal value. The materials do not state course duration, prerequisites, outcome assessment, or costs, leaving cost-benefit difficult to judge and scoring 1.
The README maps course descriptions to specific tutorial and video entries, supporting claim traceability (2). The supplied materials are mostly a README, dependency manifest, and license, with no code, tests, or independent corroboration, so cross-source corroboration scores 1. Promotional course descriptions are not separated from verifiable outcomes or explicit inferences, scoring 1.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- This is a course repository, not a complete agent implementation whose permission and side-effect controls can be assessed from the supplied materials; agents built in its tutorials require separate review.
- The dependency manifest requires Python >=3.13.9 and no installation instructions or lockfile are supplied; the materials do not establish environment reproducibility or dependency security.