Machine Learning Week
Generative AI, Predictive AI, and How They Work Together

June 3-4, 2025 l Conference
June 2 and June 5, 2025 l Workshops
Sheraton Phoenix Downtown Hotel, Phoenix, AZ

2025 KEYNOTES + SPECIAL PLENARY

Tuesday, June 3, 2025
Keynote: Five Ways to Hybridize Predictive and Generative AI

Speaker/s:

Dr. Eric Siegel, Machine Learning Week

Speaker/s:

Dr. Eric Siegel

Languages:

Keynote: Five Ways to Hybridize Predictive and Generative AI

Date:

Tuesday, June 3, 2025

Time:

8:15 am

Summary:

Predictive AI and generative AI compete for attention and budgets – but they shouldn’t. They’re two distinct categories of enterprise ML use cases that solve different problems. Predictive AI improves existing large-scale operations, while genAI generates new content items and code, answers questions, serves as a thought partner, etc.

But a marriage of the two is ushering in new value propositions. For example, predictive AI promises to actualize genAI’s ambitious claims of potential autonomy (removing or reducing the human in the loop). And genAI improves the use of predictive AI by helping code it, helping explain it to stakeholders, and more.

That’s only the tip of the iceberg. Before track 2’s breakout sessions covering such hybrid approaches, come to this keynote session for Machine Learning Week Founder Eric Siegel’s overview of this trend.

Wednesday, June 4, 2025
Keynote: Optimizing The New York Times subscription funnel using real-time causal machine learning

Speaker/s:

Rohit Supekar, The New York Times

Speaker/s:

Rohit Supekar

Languages:

Keynote: Optimizing The New York Times subscription funnel using real-time causal machine learning

Date:

Wednesday, June 4, 2025

Time:

8:50 am

Summary:

The New York Times (NYT) launched its paywall in March 2011, beginning its journey as a subscription-first news and lifestyle service. From its inception, this metered access service was designed so that non-subscribers could read a fixed number of articles every month. With the NYT’s technological transformation into a data-driven digital company, we now successfully develop models that intelligently gateway the most worthwhile pageviews in real time within milliseconds. This decision is made to maximize a combination of business objectives such as subscriptions, user engagement, and search visibility, while balancing the tradeoff between them.

This talk will begin with historical context on the digital transformation of The Times and its subscription funnel strategy with users moving from unregistered to registered and subscribed states. Following an introduction of the data science group, we will draw on differences between predictive and prescriptive machine learning models. We will then discuss how Randomized Control Trials (RCTs) can be designed to train causal machine learning models for prescriptive decision making. Modeling details about our real time models for registered and unregistered users will be discussed, highlighting the delicate balance of multiple objectives using Pareto optimization techniques. We will also present engineering technologies utilized at The Times to deploy these models for high throughput inference.

Wednesday, June 4, 2025
Special Plenary Session: The Twin Crisis in Science (and how to defeat them)

Speaker/s:

Dr. John Elder, Elder Research

Speaker/s:

Dr. John Elder

Languages:

Special Plenary Session: The Twin Crisis in Science (and how to defeat them)

Date:

Wednesday, June 4, 2025

Time:

1:15 pm

Summary:

The crises are:

  1. Most research results are false.
  2. Most discoveries never get implemented

(Perhaps, in light of the first, the second is less bad? 🙂 )

The shocking extent of these problems may lead to despair.

But hope should triumph as attainable solutions to the crises are explained.

What You Can Expect at MLW 2025

Deep dive into Generative AI, Predictive AI, and How They Work Together. Hundreds of data scientists, analytics managers and AI visionaries from manufacturing, logistics, marketing, e-commerce, financial and many more sectors will meet for keynotes, case studies and workshops over 4 days.

We provide a platform for the data science community to share success stories and insights with their industry peers. Don’t miss four days that provide the perfect opportunity for in-depth knowledge-sharing, interactive, expert discussions and intensive industry networking. Join the conference.

MLW 2025’s program is organized by the following track topics:

DAY 1:
Track 1: BizML – Business leadership for ML operationalization
Track 2: Hybrid – Combining predictive/generative AI
Track 3: AI use cases – cross-industry deployment

DAY 2:
Track 1: Predictive AI – methods and use cases
Track 2: Generative AI – methods
Track 3: Generative AI – use cases

 

Click here to view in detail the complete 2025 conference program

WITNESS HOW PRACTITIONERS AT THESE LEADING ENTERPRISES (AND MORE) APPLY MACHINE LEARNING:

2025 Workshops

On demand
ONLINE: Machine Learning Leadership and Practice: End-to-End Mastery
Eric Siegel
Monday, June 2, 2025
Machine Learning Operationalized: How BizML and Decision Modeling Get ML Deployed
James Taylor
Thursday, June 5, 2025
Automating Building of Predictive Models: Predictive AI + Generative AI
Dean Abbott
Monday, June 2, 2025
The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques
Dr. John Elder

Machine Learning Week — the facts:

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Featured Speakers 2025

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All Registrants Receive a Free Copy of The AI Playbook

Mastering the Rare Art of Machine Learning Deployment

Reviews of  MLW Founder Eric Siegel’s new book

“Set aside the hype and focus on getting things to work in practice. This is a crisp, necessary, and deeply helpful guide to getting things done with AI. Essential reading.”

—Mustafa Suleyman, Co-founder & CEO, Inflection AI and author of The Coming Wave

​

“In this book, Eric brings machine learning to life and provides a roadmap for how to operationalize it in the real world.”

—Will Lansing, CEO, FICO

Register Now to Secure Your Copy

What our attendees think!

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Partners

The Venue

Sheraton Phoenix Downtown

340 NORTH 3RD STREET,
PHOENIX, ARIZONA, USA, 85004, USA

Need more information?

  • What's the relationship between Machine Learning Week, Predictive Analytics World and Deep Learning World?

    Machine Learning Week evolved from the Predictive Analytics World (PAW) conferences, which began in 2009, running in multiple cities in the US and Europe each year. From 2018, in response to vendor and attendee requests to have one place they could meet everybody, various vertical conferences (PAW Business, PAW Industry 4.0, PAW Financial, PAW Healthcare), were brought together in one mega-event in Las Vegas. This was met with an overwhelmingly positive reception from all participants. Deep Learning World was also launched as part of the family in 2018 and PAW Climate (which runs virtually) in 2021. All are now together as Machine Learning Week.

     

  • What is predictive analytics?

    Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.

    Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.

  • Is predictive analytics different from forecasting?

    Machine Learning Week often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.

    However, predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.

  • Is this a “data mining” conference?

    Yes. Data mining is often used synonymously with predictive analytics, and, in any case, predictive analytics is a type of data mining.

  • Is this a “data science” conference?

    Yes. Predictive analytics is a form of data science. Moreover, it is the most actionable form. A predictive model generates a predictive score for each individual, which in turn directly informs decisions for that individual, e.g., whether to contact, extend a retention offer, approve for credit, investigate for fraud, or apply a certain medical treatment. Rather than solely providing insights, predictive analytics directly drives or informs millions of operational decisions.

  • Is this a “big data” conference?

    Yes. Predictive analytics is a key method to truly leverage big data. At the center of the big data revolution is prediction. The whole point of data is to learn from it to predict. What is the value, the function, the purpose? Predictions drive and render more effective the millions of organizational operational decisions taken every day.

  • Is this an AI conference?

    Yes. Artificial intelligence (AI) is a broad, subjective term with many possible definitions—but by any definition, it always includes machine learning (predictive modeling) as an example of AI technology/capabilities.

  • Is Machine Learning Week run by a software vendor?

    No. Machine Learning Week provides a balanced view of predictive analytics methods and tools across software vendors and solution providers.

  • Is Machine Learning Week a research conference?

    No. Machine Learning Week is focused on today’s commercial deployment of predictive analytics, rather than academic or R&D activities. Separately, there are a number of research-oriented conferences; in predictive analytics’ commercial application, we are essentially standing on the shoulders of those giants known as researchers.

  • Are you considering new speakers for Machine Learning Week?

    For speaker information and proposal submissions, click here.

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