How to Use Weekly Narratives

Each week, you’ll be provided with a brief narrative of what to expect in the week ahead, which will be made up of four parts:

1. Overview
   1. A brief, high-level overview of the week in a nutshell.
2. Preparing for Technical Discussions [Tuesdays]
   1. Designed to give you context on the high-level concepts that we’ll cover in class. Reviewing curated materials prior to class will enhance your ability to participate during live sessions. Reviewing these resources **after the lecture will also improve memory and recall.
3. Preparing for Live Coding [Thursdays]
   1. Designed to provide you with the most important information you need to hit the ground running during the live session. Often, you will have to install packages or set up your local machine or a cloud-based virtual machine in order to complete the activity in class.
4. Supplemental Resources
   1. These materials are provided for students who prefer taking an advanced route through the course. These will help provide additional breadth or depth on weekly topics, but are not required to follow the core curriculum.

To get the most out of each week, review this document before coming to class!

Week 2 Overview

Week 2 is all about getting the big picture of the AI product development lifecycle in focus. After we have the stages of life of our AI product in mind, we get started by scoping a specific ML project to prove feasibility. This often includes consideration of any ethical concerns, as well as addressing practical matters like the ability to collect the right amount and type of data. It is also important to consider what pre-trained models are available to rapidly assemble and deploy proofs of concept or minimum viable products.

To prepare for technical discussions this week, we recommend that you review the following select materials on the AI product development lifecycle, data-centric AI, and responsible AI.

To prepare for your live coding session, check out the section below for an overview of sentiment analysis and a few key tools that we’ll be using as we get started building our first minimum viable ML product in the first few weeks

Addition supplemental materials are also provided for those who wish to go deeper into the state of AI in 2022, the startup landscape, the AI Fund ecosystem, the evolving tools landscape, and more!

Preparing for Technical Discussions

1. Read Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology

   ml-ops.org

2. Watch (~20 minutes) of Andrew Ng’s ML Project Lifecycle overview videos from the MLOps Specialization on Canvas here.

3. Read at least one of the following articles on Data-Centric AI

   1. Labeling & Crowdsourcing, from Michael Bernstein
   2. Data in Deployment, from D. Sculley
   3. Data Augmentation, from Anima Anandkumar

4. Read about the 8 Responsible ML Principles from the Institute for Ethical AI

   The Institute for Ethical AI & Machine Learning

Preparing for Live Coding