Submission deadline is November 17, 2023. Accepted speakers will be notified by January 31, 2024.
Complimentary registration: All speakers/panelists get free access to the main two-day conference program of Machine Learning Week.
Generative AI: MLW will be held alongside a new sister conference, Generative AI World. If you would like to propose a talk on genAI’s proven enterprise value, deployed use cases, or concrete metrics that demonstrate value, go ahead and do so on the MLW submission form (linked at the bottom of this webpage). GenAI World will have its own speaker call, but either way of submitting will work. You may submit to both conferences — if so, please note whether or not you’d be available to speak twice during the two-day conference, once on genAI and once on non-genAI ML.
To apply for speaking at Machine Learning Week, please read the following instructions and then click on the Call for Speakers Form underneath.
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 MLW 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.
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. Whichever term you prefer, MLW 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, Machine Learning Week is the only conference of its kind. MLW sessions and content reach:
- Across use cases – 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?
As a vendor-neutral event, MLW’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
Machine Learning Week 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 MLW 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 call-for-speakers announcements and notifications: