Intended Audience: Machine learning / predictive analytics practitioners who are interested in combining traditional approaches to building models with new generative AI technology.

Knowledge Level: Prior experience with data preparation and/or building supervised or unsupervised learning models, either using programming languages like R, Python, SAS, or GUI-based tools like KNIME, RapidMiner, WEKA, or others. Participants will have the opportunity to build generative-AI-based models using their favorite language or tool.

Workshop Description

With the emergence of AutoML and Generative AI as leading-edge and game-changing technologies, it’s natural for data scientists to ask the question, “How much can autoML do for me practically?”, and “Can generative AI actually build models for me?”. In this workshop, the question will be answered including both “pro” and the “con” sides of using autoML and Generative AI for predictive modeling. Examples with well-known datasets will be used to illustrate the concepts, and all prompts and code used in the workshop will be made available to attendees.

This workshop will include both lecture and interactive demos. Participants can run demos in parallel with the instructor during the demo portions of the workshop. Soft copies of lecture notes, data, prompts and analyses will be provided to participants for review and revision during the workshop and subsequent to the workshop.

6 hours: 4 1.5 hour parts

Part 1: Definitions

1. Definitions and Overview of Predictive Analytics, Data Science, Machine Learning, and AI

2. Overview of data to be used.

3. Initial AutoML and GenAI analyses run for a baseline

Part 2: AutoML

1. What goes into AutoML?

2. What isn’t in AutoML?

3. How does AutoML connect to Predictive AI and Generative AI?

4. Live demo of an autoML framework and what it does for us.

Part 3a: What’s Missing in AutoML

1. Types of analyses AutoML and GenAI don’t cover well.

2. Code generation via GenAI to add these to your toolkit.

Part 3b: GenAI for DataSet #1

1. Business Question to be Addressed; Target variable definition; Error metric to optimize. GenAI version of AutoML.

2. Interactive demo with class.

Part 4a: GenAI for DataSet #2

1. Business Question to be Addressed; Target variable definition; Error metric to optimize. GenAI version of AutoML.

2. Interactive demo with class.

3. AutoML deeper dive: adding “what’s missing in AutoML” to analyses.

Part 4b: Summary of the Day

1. What worked well.

2. What parts of AutoML and GenAI that remain limiting factors

Schedule (schedule may change based on conference scheduling requirements)

8:30 am Registration and Coffee
9:00am Part 1
10:30 – 11:00am AM Coffee Break

11:00am – 12:30pm: Part 2
12:30pm – 1:30pm Lunch Break

1:30pm – 3:00pm Part 3
3:00pm – 3:30pm Afternoon coffee break

3:30pm – 5:00pm Part 4
5:00pm End of the Workshop

Instructor

Dean Abbott is Chief Data Scientist for Abbott Analytics. Mr. Abbott is an internationally recognized thought leader and innovator in data science and machine learning, with nearly four decades of experience solving problems in a wide range of vertical markets, including fraud detection, customer analytics, and survey analysis for both public and private sector organizations and was the Bodily Bicentennial Professor in Analytics at the University of Virginia Darden School of Business in 2023-2024. He is frequently included in lists of the top pioneering and influential data scientists in the world. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a popular keynote speaker and bootcamp/workshop instructor at conferences worldwide and serves on advisory boards for the UC/Irvine Predictive Analytics and UC/San Diego Data Science Certificate programs. He holds a bachelor’s degree in computational mathematics from Rensselaer Polytechnic Institute and a master’s degree in applied mathematics from the University of Virginia.