Project-based, portfolio-first learning
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.
Table of Contents
- How this program works
- Prerequisites
- Week 1: Generative AI Concepts + Dev Environment Setup
- Week 2: Interacting with Large Language Models
- Week 3: Local Large Language Models
- Week 4: Applied Solutions + Extensibility
- Week 5: Retrieval Augmented Generation
- Week 6: Python Web Frameworks for APIs
- Week 7: DevOps Principles + Deploying ML APIs
- Week 8: LLM Platforms + Ship & Finalize
- The Project
- Community
There's no grading, no enrollment gate, and no required login. Everything you need to complete the program is in this repository.
There are two ways to run through it:
- Rolling (self-paced): Start whenever you want. No meetings, no cohort — you work through the 8 weeks on your own schedule and self-assess your project against the checklist.
- Cohort: Runs alongside a shared start date with a dedicated Discord channel and an end-of-run call where participants demo their projects to each other (peer feedback, not grading).
Each week lists a plain-language objective, supporting resources, and a demo video prompt. The video prompt is worth doing even outside a cohort — it's the habit that turns a week of work into a LinkedIn post and a portfolio entry.
Important
You are not required to watch or read every resource listed. Nothing here is graded. Use the content as support for your project, not as a checklist to complete for its own sake.
Note
Diversity Statement: We share a commitment to diversity and equity, removing barriers to learning so that everyone can participate fully. This program is meant to be useful to people with a wide range of backgrounds, identities, and learning styles, whether you found it through a cohort or on your own on GitHub.
Basic Linux and programming skills. You can complete all project work in either Python or Rust — you don't need both.
Linux
If you need to shore up basic Linux skills: Linux and Bash for Data Engineering · Coursera
Python
- Python Essentials for MLOps – Week 1: Introduction to Python · Coursera
- Python Essentials for MLOps – Week 2: Python Functions and Classes · Coursera
Rust
- LLMOps with Azure · Coursera
- Cloud Machine Learning Engineering and MLOps
- Rust Data Engineering · Coursera
- Cloud Computing Foundations
- Python Essentials for MLOps · Coursera
- Introduction to Generative AI · Coursera
- Public Speaking
- Developing Effective Technical Communication
- Exploring Cloud Onboarding
- Python Development Environments
- Pytest Master Class · Coursera
- Python Essentials for MLOps – Week 5: Applied Python for MLOps · Coursera
- Rust: Setting up your Rust Development environment · Coursera
Weekly demo video prompt: Discuss your plan for your individual project — what you're building and how you'll pace it over the next 8 weeks — and describe your programming language choice, including its advantages and potential pitfalls. Use the Public Speaking guidelines to deliver a great demo.
- Interacting with models · Coursera
- Building robust Generative AI systems · Coursera
- Introduction to MLOps Walkthrough
- MLOps Foundations: Chapter 2 Walkthrough of Practical MLOps
- Practical MLOps, Chapter 1: Introduction to MLOps
- Practical MLOps, Chapter 2: MLOps Foundations
Weekly demo video prompt: Explain some challenges your application will face working with LLM output, and what you'll do to mitigate them.
- Beginning Llamafile for Local Large Language Models (LLMs) · Coursera
- Getting Started with Open Source Ecosystem · Coursera
- Foundations of Local Large Language Models · Coursera
Weekly demo video prompt: Describe your evaluation of the local LLMs/SLMs available with Llamafile and which one fits your project best.
- Local LLMOps · Coursera
- AI Pair Programming from CodeWhisperer to Prompt Engineering
- Using Local LLMs from Llamafile to Whisper.cpp · Coursera
- Open Source Platforms for MLOps – Week 2: Introduction to Hugging Face · Coursera
- Open Source Platforms for MLOps – Week 3: Deploying Hugging Face · Coursera
- Open Source Platforms for MLOps – Week 4: Applied Hugging Face · Coursera
- Extending with Functions and Plugins · Coursera
- Applications of LLMs · Coursera
- MLOps Platforms: Amazon SageMaker and Azure ML – Week 1 · Coursera
Weekly demo video prompt: Walk through the architectural overview of your application and its challenges, and explain what plugins or functions would extend it — and what would make that harder in a production environment.
Weekly demo video prompt: How would adding RAG to your application change the experience for an end user? Why would you (or wouldn't you) use RAG here?
- Introduction to FastAPI Framework · Coursera
- Introduction to Flask Framework · Coursera
- Applied Python for MLOps · Coursera
- Cloud Virtualization, Containers and APIs
Weekly demo video prompt: What are the benefits of the framework you chose, and how will you wire it into your application?
- Responsible Generative AI · Coursera
- Applying DevOps Principles
- MLOps Platforms: Amazon SageMaker and Azure ML – Week 2 · Coursera
- MLOps Platforms: Amazon SageMaker and Azure ML – Week 3 · Coursera
- Introduction to GitHub Actions · Coursera
- Building an End-to-End LLM application in Azure · Coursera
- Operations
Weekly demo video prompt: How are you applying DevOps and responsible AI principles — and automation — to your application? What difficulties came up building the automation, and how will it benefit the project going forward?
- Introduction to LLMOps with Azure · Coursera
- mlflow-project-best-practices
- MLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn
- Azure Databricks, Pandas, and Opendatasets · Coursera
- MLOps Platforms: Amazon SageMaker and Azure ML – Week 4 · Coursera
- MLOps Platforms: Amazon SageMaker and Azure ML – Week 5 · Coursera
Finish the project: publish the repository, run it against the self-assessment checklist, and write a short portfolio/LinkedIn post pointing at the repo. Rolling learners get a closing email when they hit this week; cohort learners present on the end-of-run call instead of (or in addition to) posting.
Weekly demo video prompt: How would adding a cloud LLM platform change what your application can do, and what's a drawback of relying on one? Wrap with a short demo of the finished project.
The whole program is built around a single project you start in Week 1 and ship in Week 8. See the self-assessment checklist for what "done" looks like.
Important
Do not build a model yourself. Reuse an existing LLM or SLM. Use Mozilla Llamafile as a reference for running one locally.
The primary two resources for building an LLM solution on a local API:
- Beginning Llamafile for Local Large Language Models (LLMs) · Coursera
- Getting Started with Open Source Ecosystem · Coursera
- Python MLOps Cookbook
- databricks-zero-to-mlops
- Python Fire · Coursera
- Refactoring a Python script into a library called by Python Click CLI · Coursera
- Container Continuous Delivery
- Functions to Containerized Microservice Continuous Delivery to AWS App Runner with Fast API · Coursera
- mlflow-project-best-practices
- databricks-zero-to-mlops
- Python MLOps Cookbook
- Edge Computer Vision
- GitHub Codespaces
- AWS Academy
- Azure for Students
- Google Qwiklabs
- Practical MLOps
- Pragmatic AI
Optional supplementary readings & media
- AWS Bootcamp
- Python for DevOps · Coursera
- Practical MLOps
- Cloud Computing for Data Analysis
- Pragmatic AI: An Introduction to Cloud-Based Machine Learning
- AWS Training & Certification
- AWS Educate
- AWS Academy
- Google Qwiklabs – Hands-On Cloud Training
- Microsoft Learn
- Applied Computer Vision with Python Lectures
- Learn Python in One Hour · Coursera
- Cloud Computing with Python
- Python for Data Science with Colab and Pandas in One Hour · Coursera
- GCP Cloud Functions
- Azure AutoML
AWS
- AWS Certified Cloud Practitioner Video Course
- Cloud Computing for Data
- AWS in One
- AWS Certified Machine Learning – Specialty video course · Coursera
- AWS Machine Learning · Coursera
- AWS Solutions Architect
GCP
Python
- Data Science, Pandas, and Colab · Coursera
- Python and DevOps · Coursera
- Python Command-line Tools · Coursera
Linux and Systems Engineering
Join the shared PAIML Discord to ask questions, share progress, and post your weekly demos. (Discord invite link goes here.) If you're in a cohort, you'll also get invited to a dedicated channel for your run, alongside the shared server.