Description and Objectives

AI Engineering (a.k.a applied ML) focuses on deploying AI systems in production and building E2E workflows that modularizes the ML training and inference lifecycles. According to the 2015 NIPS paper “Hidden Technical Debt in Machine Learning Systems” by Sculley et al., a mature ML system may consist of only 5% ML code, with the remaining 95% being “glue code” that integrates various components. This course aims to explore the different aspects of this “glue code” and introduce participants to widely adopted practices for building and maintaining inference and training pipelines for their businesses. The course will alternate between lecture slides and hands-on demonstrations, with one week dedicated to slides content and the next to practical application.


Hardware Requirements

A decent PC or laptop with internet access, and the ability to use Google Colab for examples when needed


Course Schedule

The course consists of 12 one-hour weekly lectures