The premier machine learning event –
select a conference:

PAW Healthcare
PAW Business
PAW Industry 4.0
PAW Financial
PAW Government
Deep Learning World

The premier machine learning event –
select a conference:

7 Parallel Running Tracks

150+ International Speakers

150+ Cutting Edge Sessions

Predictive Analytics World is the leading cross-vendor conference series covering the commercial deployment of machine learning and predictive analytics.

Predictive Analytics World — the facts:




Total Events







Machine Learning Week 2020 Keynotes Include:

A Charles Thomas

A Charles Thomas
Chief Data & Analytics Officer
General Motors

Becoming Data Driven in the Automotive Industry

Keynote Details

Becoming Data Driven in the Automotive Industry
Drawing from his experience as the chief data and analytics officer at three different companies, A. Charles Thomas – now chief data and analytics officer at General Motors – will share insights and lessons learned from both sides of the unique, two-pronged role he plays at GM.

First, Charles’ team leverages analytics to enhance GM’s traditional businesses, such as selling vehicles, OnStar, Warranty, SiriusXM, and others. The team generates insights to drive billion-dollar improvements in functions such as manufacturing, HR, Marketing, and Digital.

Second, Charles’ team also drives revenue from their unique access to tremendous quantities of vehicle data. This includes direct licensing of connected vehicle data (e.g. GPS data to traffic and parking apps, media, retail, and insurance companies), as well as using these data to create new businesses in insurance, fleet management, and others.

In this keynote address to both the PAW Business and PAW Industry 4.0 audiences, Charles will share his unique insider’s vantage.

Gil Arditi

Gil Arditi
Product Lead, Machine Learning


From Self-Driving to Fraud Detection – How Lyft Streamlines Machine Learning Deployment

Keynote Details

From Self-Driving to Fraud Detection – How Lyft Streamlines Machine Learning Deployment
In this keynote address, Gil Arditi will cover the areas of machine learning development at Lyft, talk about friction points in the model lifecycle – from prototyping and feature engineering to production deployment – and show how Lyft streamlined this process internally. He will also cover a step-by-step example of a model that was recently developed and taken to production.

Jen Gennai

Jen Gennai
Head of Responsible Innovation, Global Affairs
Putting Ethical Principles into Practice When Deploying Machine Learning

Keynote Details

Putting Ethical Principles into Practice When Deploying Machine Learning
As principles purporting to guide the ethical development of Artificial Intelligence proliferate, there are questions on what they actually mean in practice. How are they interpreted? How are they applied? How can engineers and product managers be expected to grapple with questions that have puzzled philosophers since the dawn of civilization, like how to create more equitable and fair outcomes for everyone, and how to understand the impact on society of tools and technologies that haven’t even been created yet. To help us understand how Google is wrestling with these questions and more, Jen Gennai, Head of Responsible Innovation at Google, will run through past, present and future learnings and challenges related to the creation and adoption of Google’s AI Principles.

Jennifer Lewis Priestley

Jennifer Lewis Priestley
Professor of Applied Statistics and Data Science

How Leading Enterprises Leverage Universities to Boost Analytical Innovation and Tap Talent

Keynote Details

How Leading Enterprises Leverage Universities to Boost Analytical Innovation and Tap Talent
How many .edu addresses are in your inbox right now? As organizations pursue digital transformation strategies, challenges related to finding and retaining analytical talent, objectively assessing the relevance of new, and emerging technology and engaging in deep and meaningful innovation with eventual payback are common to all sectors of the economy. Deep, collaborative partnerships with universities can help mitigate many of these challenges. This is all the more true because data science itself has given rise to a new “entrepreneurial university” paradigm. Dr. Priestley is an academic Associate Dean, who worked for organizations like Accenture and VISA EU, and now manages corporate partnerships with the likes of Blue Cross Blue Shield, Emerson, Equifax, and GE, as well as fire departments and law enforcement. She will discuss the ways that organizations should be thinking about working with universities, but typically don’t — including research, innovation, “externships,” training options, recruitment, and other strategic relationships. After this session, you will never look at universities the same way again.

Impressions from Machine Learning Week


James McCaffrey- Senior Scientist Engineer, Microsoft

The bottom line: the event was really good — I give it an overall grade of an A- which is (tied for) the best grade I’ve ever given to any conference.

Indu Sriram - Digital Marketing Analytics Manager, Staples

I'm happy we have a conference like Predictive Analytics World - where practitioners like myself can meet other professionals and learn all the latest and greatest. It's a go-to resource and I often attend - hats off to this conference's producers!

Allison Gonzalez - Decision Science Analyst, USAA

Just do it! Everybody is doing it! I attended PAW San Francisco 2016 and I come back with many new contacts, new friends, and more knowledgeable.

Kenton - Economist, Nike

The emphasis on practical application of analytics to real world business problems and decision making is just right at this conference!

Jason King - Principal Scientist, Procter and Gamble

A 360 degree event - great for anyone who wants to know where data analytics is at and where it's going.


Deliver on machine learning’s full potential in your industry by attending a specialized PAW event:

Predictive Analytics World Business

Predictive Analytics World for Business focuses on concrete examples of deployed predictive analytics. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors and other top practitioners deploy machine learning, and the kind of business results they achieve.

Predictive Analytics World Business in London

OCTOBER 16-17, 2019

Predictive Analytics World Business in Berlin

NOVEMBER 18-19, 2019

Predictive Analytics World Business in Las Vegas

MAY 31 – JUNE 4, 2020

Predictive Analytics World Financial

Predictive Analytics World for Financial Services is the leading data science event covering the deployment of machine learning by banks, insurance companies, credit card companies, investment firms, and other financial institutions.

Predictive Analytics World Financial in Las Vegas

MAY 31 – JUNE 4, 2020

Predictive Analytics World Healthcare

The PAW Healthcare program will feature sessions and case studies across Healthcare Business Operations and Clinical applications so you can witness how data science and machine learning are employed at leading enterprises, resulting in improved outcomes, lower costs, and higher patient satisfaction.

Predictive Analytics World Healthcare in Las Vegas

MAY 11 – May 12, 2020

Predictive Analytics World Healthcare in Las Vegas

MAY 31 – JUNE 4, 2020

Predictive Analytics World Industry 4.0

Predictive Analytics World for Industry 4.0 is the leading vendor-neutral conference for machine learning for smart manufacturing and IoT. Data scientists, industrial planners, and other machine learning experts will meet in Las Vegas on May 31-June 4, 2020 to explore the latest trends and technologies in machine & deep learning for the IoT era.

Predictive Analytics World Industry 4.0 in Munich

MAY 11-12, 2020

Predictive Analytics World Industry 4.0 in Las Vegas

MAY 31 – JUNE 4, 2020

Data Driven Government

Data Driven Government is focused on helping government executives to share and discuss emerging trends and best practices of how government agencies are currently using data analytics to enhance mission outcomes. Practically-focused, vendor-neutral, DDG advances the deployment of analytics and data science within Federal, State, and Local government.

Predictive Analytics World Government in Washington DC


Deep Learning World

Deep Learning World is the premier conference covering the commercial deployment of deep learning. The event’s mission is to foster breakthroughs in the value-driven operationalization of established deep learning methods.

Deep Learning World in Berlin

MAY 11-12, 2020

Deep Learning World in Las Vegas

MAY 31 – JUNE 4, 2020

Training Workshops – June 2020

Sunday, May 31, Las Vegas
Big Data: The Leading Ways to Improve Business with Data Science (Non-Technical)
Marc Smith, Chief Social Scientist, Connected Action Consulting Group

Sunday, May 31, Las Vegas
Machine Learning with R: A Hands-On Introduction
Robert A. Muenchen, Manager of Research Computing Support, University of Tennessee

Monday, June 1, Las Vegas
The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques
John Elder, Founder & Chair, Elder Research

Monday, June 1, Las Vegas
Deep Learning in Practice: A Hands-On Introduction
James McCaffrey, Senior Scientist Engineer, Microsoft Research

Monday, June 1, Las Vegas
Machine Learning with Python: A Hands-On Introduction
Clinton Brownley, Data Scientist, WhatsApp

Monday, June 1, Las Vegas
Machine Learning Operationalized for Business: Ensuring ML Deployment Delivers Value
James Taylor, CEO, Decision Management Solutions

Thursday, June 4, Las Vegas
Ensemble Models: Supercharging Machine Learning
Dean Abbott, Co-Founder and Chief Data Scientist, SmarterHQ

Thursday, June 4, Las Vegas
Spark on Hadoop for Machine Learning: Hands-On Lab
James Casaletto, Senior Solutions Architect, MapR Technologies

Thursday, June 4, Las Vegas
The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them
John Elder, Founder & Chair, Elder Research

Thursday, June 4, Las Vegas
Hands-On Deep Learning in the Cloud: Fast and Lean Data Science with Tensorflow, Keras, and TPUs

Online Workshop
Predictive Analytics Applied – An Online Introduction
Eric Siegel, Ph.D., Conference Founder, Predictive Analytics World

More about Predictive Analytics World, Machine Learning and AI

Conference Scope

Whether you call it machine learning, predictive analytics, data science, big data, data mining or artificial intelligence, Predictive Analytics World sessions cover the commercial applications of machine learning, across business, finance,

marketing, manufacturing, healthcare, recruitment, government, including the following:

  • Targeting marketing (offline and online)
    • Response modeling
    • Customer retention with churn modeling
    • Acquisition of high-value customers
    • Direct marketing
    • Database marketing
    • Profiling and cloning
  • Online marketing optimization
    • Behavior-based advertising
    • Email targeting
    • Website content optimization
  • Methods Covered
    • decision trees
    • logistic regression
    • neural networks
    • net lift modeling
  • Product recommendation systems
  • Workforce analytics
  • Fraud detection
  • Insurance pricing and selection
  • Credit scoring
  • Other forms of risk management
  • Predictive maintenance
  • Logistics analytics
  • Fault prediction & failure detection
  • Anomaly detection & root cause analysis
  • Supply chain connectivity and optimization
  • Risk management & prevention
  • Smart grid, utilities, and energy operations
  • Credible “AI” approaches with demonstrated value
  • Image & video recognition
  • Internet of Things & smart devices
  • Stream mining & edge analytics
  • Machine learning, ensemble models, & deep learning
  • Process mining & network analyses
  • Mining open & earth observation data
  • Edge analytics & federated learning
  • And more

Predictive Analytics Guide

Predictive analytics is information 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. For more answers on data science visit the Predictive Analytics Guide for reading, training options and other resources.

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