news.qmd

# Updates {.unnumbered}

-   [03-January-2025] updated [MLOps Stack](./ch00/mlops-stack.qmd) page
-   [01-January-2025] Material launched based on [AI-839](https://mlsquare.github.io/ai-839/) taught at [IIIT-B](https://www.iiitb.ac.in/) in the Fall of 2024.



## Overview

- See [preface](./preface.qmd) for why MLOps and the approach and outlook taken here.
- See the [Full Stack MLOps](./index.qmd) page for recent information on Lecture Notes, Homeworks, Projects, etc..

**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 (ML Engineering)**

-   Topics
    -   MLOps motivation, need
    -   Basic principles and MLOps with Open Source Software
-   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 metric
-   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.
    -   frame, discover, develop, deploy, monitor, improve, re-deploy and maintain an ML Application