With these five conferences co-located, attendees gain the advantages of both a focused, niche event (depending on which one(s) they attend), as well as the advantages of a large, all-hands event, such as the industry-leading super-plenary keynotes, cross-vertical ML expo, wide array of venders, and maximal networking benefit.
Extended Submission deadline is December 16, 2022. Accepted speakers will be notified by January 31, 2023.
Complimentary registration: All speakers/panellists get free access to all five co-located conferences during the main two-day program of Machine Learning Week Conference.
To apply for speaking at Machine Learning Week Las Vegas, please read the following instructions and then click on the Call for Speakers Form underneath. (To apply to speak at Machine Learning Week Europe please click here).
Maximize Your Chances of Being Accepted by Following these Recommendations:
All speakers: Please read this call for speakers in its entirety before proceeding to the speaker proposal form (below).
Software vendors: If you are employed by a software vendor, read this restriction on speaking.
Join Machine Learning Week to share how predictive analytics and machine learning deliver a business, operational or clinical impact for your organization. Presenting at PAW is a fulfilling way to engage with the leading cross-vendor community of the field, and provides complimentary registration/access to the main two-day program of Machine Learning Week, across all its component conferences.
Join an elite crowd. Prior Machine Learning Week speakers have included:
- Uber: Mike Tamir, Head of Data Science, ATG
- Caterpillar: Morgan Vawter, Chief Analytics Director
- Dell EMC: Theresa Kushner, Sr VP, Performance Analytics Group
- Capital One: Kate Highnam, Machine Learning Engineer
- Elder Research: John Elder, Founder & Chair
- Northern Trust: Andy Curtis, Senior VP
… plus leading practitioners presenting on deployment case studies from Becker College, Central Pacific Bank, Cisco, Comcast, Google, Hitachi, IBM, John Hancock, Lyft, Northwestern Mutual, Quicken Loans, Seagate, Shell, Turner, Twitter, Verizon, and more.
This event covers machine learning, which is essentially synonymous with predictive analytics. Although “machine learning” used to be common only within the walls of research labs, it’s now also used more and more in the context of commercial deployment. Whichever term you prefer, PAW covers technology that learns from data to predict or infer an unknown, including decision trees, logistic regression, neural networks, and many other methods.
The premier cross-vendor machine learning event focused on commercial/operational deployment, Predictive Analytics World is the only conference of its kind. PAW sessions and content reach:
- Across applications – For what purpose is machine learning deployed?
- Across industries – Where is machine learning deployed?
- Across vendors of solutions and software – How is machine learning deployed?
Please read more about the scope, objective and target audience of this conference on the about and FAQ pages.
Restriction for Analytics Software Vendors
As a vendor-neutral event, PAW’s core program is booked exclusively with enterprise practitioners, thought leaders and adopters, with no predictive analytics software vendors eligible to present or co-present. If you are employed by or represent an analytics software vendor, a vendor of a software solution designed to support the development or deployment of analytics (regardless of whether the solution itself generates the analytical model or analytical component to be deployed), or a company with webpages or materials that gives the clear impression you sell an analytics software solution, then you are not eligible to submit the speaker proposal form below. As an alternative, you are encouraged to consider Becoming a Sponsor, and/or to suggest your clients submit a proposal to speak (point them to this web page).
Present Your Case Studies
Predictive Analytics World provides speakers the opportunity to present machine learning case studies, deployment successes and lessons learned. At this event, potential consumers of machine learning witness proof demonstrating it’s more than just a bunch of great ideas – machine learning is actively applied to optimize many business functions across industry verticals. And machine learning practitioners have the opportunity to gain from the lessons you’ve learned, whether by serendipity, or – more likely – the hard way.
What about presentations on methodology? A proven methodology can be an important contribution well worth sharing at MLW – including both technical approaches, and business-side organizational processes related to ML deployment. Either way, we encourage you to consider incorporating your deployed “case study” results into such a presentation. MLW emphasizes deployment results as an important way to more fully demonstrate end-to-end evidence of a novel method’s value.
Evaluation – how well did it work? Case study proposals will be given highest consideration if specific measurements of deployment performance are included, especially when measured in comparison to a control group.
Before submitting the speaker form, carefully read the terms listed there in detail. They are not only “legalese” meant to protect PAW from arcane legal exposures – rather, they are to protect the event’s value! You must read and understand each one, since they ensure that the pre-event planning process, its marketing, and the event itself are as high-caliber as possible.
These terms stipulate 1) that you have pre-established authorization from your employer to present, 2) that you have your travel expenses covered, and 3) that you agree if accepted not to cancel other than for medical or family-emergency reasons – among other requirements. Submitting and agreeing to speak is a professional committment to be taken seriously.
Sign up for Machine Learning Week / PAW call-for-speakers announcements and notifications:
For details about speaking at other machine learning events in Europe and for Government see:
In addition to submitting a proposal to speak at PAW, you can also consider writing for The Machine Learning Times.