Project-based, Portfolio-first

Build a real application over 8 weeks and ship it publicly — no grading, no enrollment gate.

8

Weeks

45

Video Resources

24

Hands-on Tasks

Weekly reminders

Email reminders each week once you join, starting one week out

Weekly demo prompts

Turn each week's work into a portfolio-ready update

Open on GitHub

https://github.com/paiml/llmops-applied-program

Weekly Syllabus

8 weeks, one project, shipped publicly

Week 1

Generative AI Concepts + Dev Environment Setup

Choose the problem your project will solve, and who it's for
Choose your programming stack — Python or Rust
Set up your dev environment and initialize the project repository

Week 2

Interacting with Large Language Models

Decide how you'll interact with the SLM/LLM (direct API calls, an SDK, a local server's HTTP endpoint, etc.)
Experiment with prompts against a model and note the failure modes your application will need to handle (bad output, timeouts, hallucination)
Sketch the input/output contract your application expects from the model

Week 3

Local Large Language Models

Evaluate the local LLMs/SLMs available to you (Llamafile, Ollama, llama.cpp, etc.) against your problem
Choose the model and serving tool for your project
Get it running locally and confirm you can call it programmatically from your application's stack

Week 4

Applied Solutions + Extensibility

Build the core application logic around the model — input handling, prompt construction, output parsing, error handling
Identify what plugins or functions would extend your application, and pick one to prototype
Sketch an architectural overview of your application

Week 5

Retrieval Augmented Generation

Decide whether RAG fits your project — not every project needs it
If it does, implement a basic retrieval pipeline over your document set
If it doesn't, document why, so the decision shows up in your project's history and demo

Week 6

Python Web Frameworks for APIs

Choose a web framework (FastAPI, Flask, or a Rust equivalent) if your interface needs one — skip this if your interface is a CLI
Wire the framework into your existing application logic
Expose your application's endpoints and confirm they work end to end

Week 7

DevOps Principles + Deploying ML APIs

Containerize the application, without baking the model into the image
Set up GitHub Actions CI to build the container image on push
Apply and document basic DevOps and responsible-AI practices for your project

Week 8

LLM Platforms + Ship & Finalize

Evaluate what a cloud LLM platform would change about your application, as a point of comparison to your local/self-deployed setup
Run your project against the self-assessment checklist
Publish the repository publicly and write a short portfolio/LinkedIn post linking to it

Frequently Asked Questions

Everything you need to know about the LLMOps Applied Program

No. There's no grading, no enrollment gate, and no required login to work through the program's repository. Joining here just sets up weekly email reminders and an optional calendar schedule.

By default, your Week 1 reminder and calendar event land one week after you join — giving you time to prep. You can pick a custom start date for your calendar invite using the date field above.

Yes — use the same button to leave at any time, which stops the weekly email reminders.