Welcome to
Machine Learning Week 2024
The premier machine learning event

June 6-7, 2024 l Conference
June 4-5, 2024 l Workshops
Sheraton Phoenix Downtown Hotel, Phoenix, AZ

 

2024 KEYNOTES + SPECIAL PLENARY

Thursday, June 6, 2024
Keynote: How Transformers Reinvent Machine Learning – for Both Generative AI and Predictive AI

Speakers:

Julien Simon

Keynote: How Transformers Reinvent Machine Learning – for Both Generative AI and Predictive AI

Date:

Thursday, June 6, 2024

Time:

8:40 am

Room:

Phoenix Ballroom D

Summary:

In this keynote, you’ll learn how open-source models can help you build high-quality AI applications, generative or not, while giving you more flexibility, control, and ROI than closed-model APIs. We’ll highlight the latest and greatest models, and show you how to get started with them in minutes. Along the way, you’ll also learn about the technical ecosystem that Hugging Face is fostering, from models and datasets, to cloud integrations and hardware acceleration.

Thursday, June 6, 2024
Keynote: AI into Business Impact at Global Scale

Speakers:

Morgan Vawter

Keynote: AI into Business Impact at Global Scale

Date:

Thursday, June 6, 2024

Time:

1:30 pm

Room:

Phoenix Ballroom D

Summary:

Join this keynote address from Unilever Global VP of Data & Analytics Morgan Vawter to learn about how Unilever has scaled AI capability and embedded it in the business to improve supply chain transparency, drive joint growth with customers, and improve consumer experience.

Check out Morgan Vawter’s Machine Learning Times article, Three Best Practices for Unilever’s Global Analytics Initiatives.

Thursday, June 6, 2024
Keynote: Responsible AI: A Practical Guide

Keynote: Responsible AI: A Practical Guide

Date:

Thursday, June 6, 2024

Time:

4:45 pm

Room:

Phoenix Ballroom D

Summary:

One of the biggest challenges facing AI today is the fact that, despite volumes of guidance detailing how AI should be safe, ethical, and responsible, many organizations don’t have established standards and practices to enforce responsible AI.  Responsible AI requires firm model development governance standards, including algorithms allowed, processes followed, testing completed, and auditability to ensure accountability to meeting responsible AI standards. In this keynote address, FICO CAO Scott Zoldi will discuss three pillars of Responsible AI: explainable AI, ethical AI, and auditable AI in the context of life-altering decisions derived from AI models. Establishing a corporate model development standard and enforcing adherence through auditable AI not only allows meeting regulatory rules and guidance, but further establishes customer trust and safe usage of AI.

Thursday, June 6, 2024
Keynote: The BizML Playbook for Getting Machine Learning Deployed

Keynote: The BizML Playbook for Getting Machine Learning Deployed

Date:

Thursday, June 6, 2024

Time:

9:10 am

Room:

Phoenix Ballroom D

Summary:

The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption.

In this keynote, Machine Learning Week Founder Eric Siegel presents a six-step practice called bizML for ushering machine learning projects from conception to deployment. This disciplined approach, which is the topic of Siegel’s new book, The AI Playbook, serves both sides: It empowers business professionals and it establishes a sorely needed strategic framework for data professionals.

Friday, June 7, 2024
Keynote: 10 Steps to Innovation – It’s Math and Psychology

Speakers:

Jack Levis

Keynote: 10 Steps to Innovation – It’s Math and Psychology

Date:

Friday, June 7, 2024

Time:

8:55 am

Room:

Phoenix Ballroom D

Summary:

As a UPS Manager of 43 years, Jack Levis witnessed tremendous change.  UPS became an Airline, a Global Logistics Player, and integral to the E-Commerce explosion. Of all the changes Jack witnessed, Data and Technology was arguably the largest and most impactful. As a Senior Director responsible for the Digitization and Advanced Analytics of Operations, Jack oversaw much of that technology transformation. The impact was tremendous with many hundreds of millions of dollars in cost savings while simultaneously providing additional services to customers.

In this talk, Jack will discuss lessons learned in successfully managing the breakthrough innovation.  While the tools, algorithms, and mathematics are important, he will also discuss the need for psychology in terms of culture, leadership, and change management. Using examples from his journey, Jack will describe Ten Steps to Innovation through Math and Psychology. 

Friday, June 7, 2024
Special Plenary: The Reliability of Backpropagation is Worse than You Think

Special Plenary: The Reliability of Backpropagation is Worse than You Think

Date:

Friday, June 7, 2024

Time:

1:15 pm

Room:

Phoenix Ballroom D

Summary:

Neural Networks are flexible, powerful, and often very competitive in accuracy.  They are criticized primarily for being uninterpretable black boxes, but their chief weakness is that backpropagation makes them unrepeatable.  That is, their final coefficient values will differ, from one run to the next, even if the NN structure, meta-parameters, and data are held constant!  And unlike multi-colinear regressions, the varied NN coefficient sets aren’t just alternative ways — in an over-parameterized model — of producing similar predictions.  Instead, the predictions can vary a disquieting amount and often “converge” to a significantly worse training fit than is possible.  

What happens if one instead employs a global optimization algorithm to train a NN?  Untapped descriptive power should be unleashed, encouraging use of simpler structures to avoid overfit.  And, with randomness removed, the results will be repeatable.  We’ll demonstrate initial results for (the relatively small) NNs practical to optimize.

 

What You Can Expect at MLW 2024

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 2024’s program is organized by three track topics:

Track 1: BizML – Business practice for ML operationalization

Track 2: Tech – Advanced ML methods & MLOps

Track 3: ML Use Cases – Cross-industry deployment

 

Click here to view in detail the complete conference program

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

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

Machine Learning Week — the facts:

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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.

     

  • 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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