index.qmd

# Welcome {.unnumbered}


Hello Students of AI 839.

See the [course](./course.qmd) page for recent information on Lectures, Homeworks, Projects, etc.. 

## Announcements
- [01-August-2023] Course website up


## Overview

**Prereqs**

- Exposure and skill in data handling, building models in Python, PyTorch
- Exposure and skill in developing code using Python, Git, IDEs like VS Code
- A foundation course in Machine Learning, Deep Learning, Data Modeling, working with (Big) Data


**Part-1: Essentials**

- Topics
    - basic principles and MLOps with Open Source Software
    - three assignments
- Learning Outcomes: students will be able to
    - deploy models with logging, documentation, unit tests, and APIs
    - understand a conceptual framework to approach MLOps holistically 

**Part-2: Full Stack MLOps**

- Topics
    - holistic understanding of ML development, beyond chasing typical performance metrics
    - one assignment, one mini project and a midterm
- Learning Outcomes: students will be able to
    - deploy models, observe their performance, make improvements, redeploy them.  
    - ensure that the ML pipeline is reproducible. 
    - incorporate principles from Responsible AI and build ML systems which can consist of many models and tools.    

**Part-3: Intro to LLMOps & Application**

- Topics
    - practice, cloud solutions
    - capstone project and presentations
    - invited lectures from Industry
- Learning Outcomes: students will be able to
    - frame, discover, develop, deploy, monitor, improve, re-deploy and maintain an ML Application
    - approach the problem holistically, optimize RoI