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 Generative AI as a leading-edge and game-changing technology, it’s natural for data scientists to ask the question, “Can generative AI help me?”. In this workshop, the question will be answered including both “pro” and the “con” sides of using Generative AI for predictive modeling – often now labelled as Predictive AI. 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 a hands-on portion.

6 hours: 4 1.5 hour parts

Part 1: Definitions

1. Different Flavors of AI

• a. Predictive AI -> Predictive Analytics -> Data Science -> Machine Learning
  b. Generative AI: Deep Learning and LLMs
  c. Why predictive AI is only a new marketing label for pre-existing technology
  d. Automation: AutoML

2. Example Use Cases for illustration

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?

Part 3: Build a predictive model using Generative AI (HANDS ON)

1. Define the problem: Business Understanding

2. How Large Language Models can be applied for predictive use cases.

3. Hands-on exercise

• a. Prompt
  b. Run code
  c. Identify deficiencies
  d. Repeat

4. Summarize results

Part 4: Build a Predictive AI model

This exercise will involve a different modeling software tool than in Part 3.

• a. Use prebuilt workflow elements but all “by hand”
  b. Run model. Assess results
  c. Identify deficiencies
  d. Compare to Generative AI above

Conclusions:

1. What does Generative AI for Predictive AI do well? What doesn’t it do well? Why or why not?

2. What does AutoML do well and what does it miss?

Schedule

8:30am MST Workshop starts
10:00 – 10:15am MST AM Break
12:00am – 12:45pm MST Lunch Break
2:15 – 2:30pm MST PM Break
4:30pm MST End of the Workshop

Instructor

Dean Abbott is an internationally recognized thought leader and innovator in data science and predictive analytics, with more than three decades of experience solving problems in customer analytics, fraud detection, risk modeling, text mining, survey analysis, and many more. 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 bachelors degree in computational mathematics from Rensselaer Polytechnic Institute and a masters degree in applied mathematics from the University of Virginia.