Applied Program

LLMOps Applied Program

An 8-week, project-based, portfolio-first program on operationalizing Large Language Models (LLMOps). Build a real LLM application, ship it publicly, and walk away with a portfolio piece, not a grade.

Join this program 8 Weeks • Free • Project-based

Starts 1 week from today — add to your calendar:

View Syllabus

Project-based, Portfolio-first

Build a real application over 8 weeks and ship it publicly — no grading, no enrollment gate. Everything you need is in the public GitHub repository.

8
Weeks
24
Hands-on Tasks
45
Video Resources
Weekly reminders

Join and get email reminders each week, 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

What You'll Gain

Skills and roles this program prepares you for

Potential Roles

LLMOps Engineer
MLOps Engineer
AI Application Developer
Machine Learning Engineer
Platform Engineer

Skills You'll Gain

Large Language Model Integration
Prompt Engineering
Local LLM Deployment (Llamafile, Ollama, llama.cpp)
Retrieval Augmented Generation (RAG)
Python or Rust Application Development
API Development (FastAPI/Flask)
Containerization & CI/CD
Responsible AI Practices

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

Everything above is free and public in the GitHub repository — joining just sets up your weekly reminders and calendar.

Join this Program Free • Weekly Email Reminders

Prerequisites

Basic Linux and programming skills. All project work can be completed in either Python or Rust — you don't need both.

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.

Both. Rolling/self-paced means you start whenever you want with no meetings. Cohort runs alongside a shared start date with a dedicated Discord channel and an end-of-run demo call.

No — the "Add to calendar" button above works without logging in, defaulting to a schedule starting one week from today, or a custom date you choose.