Speak at ML Week’s
HYBRID AI 2026
The Clift Royal Sonesta
San Francisco, CA, May 5-6, 2026

Call for Speakers and Save-the-Date:

ML Week’s
HYBRID AI 2026

Live in San Francisco, CA, May 5-6, 2026

For 2026, Machine Learning Week returns to San Francisco, as HYBRID AI 2026. This is MLW’s 18th year, and perhaps its most important.

Submission deadline: November 14, 2025. Accepted speakers will be notified by January 12, 2026.

Complimentary registration: All speakers get free access to the main two-day conference program.

2026 event theme: Hybrid AI

AI is on the cusp of greatness. The bad news is that positive returns are still few and far between – begging the question, when will AI finally achieve its greatness? The good news is that the final mile to more universally realized value is in sight.

THE PROBLEM: How can practitioners get genAI pilots to production – and get predictive AI from development to deployment – considering that the success rates are still extremely low?

THE SOLUTION:

1) Hybrid AI. GenAI and Predictive AI are destined to marry because each is suited to address the other’s greatest limitations: GenAI is often unreliable, while predictive AI is hard to use.

2) A reliability layer to tame LLMs. This layer must feature:

i) Continually expanding guardrails
ii) Strategically embedded humans in the loop – indefinitely
iii) Form-fitted customization for each project

Why hybrid AI? The reliability layer demands a strategic hybridization of methods – such as predictive AI and genAI – as well as the strategic embedding of humans-in-the-loop (human/machine collaboration is also sometimes called “hybrid AI”).

The most ideal way to soften the AI bubble’s looming detonation would be to boost AI’s realized value. To this end, developing a reliability layer is a critical, emerging discipline. It’s vital for establishing system robustness that would make AI pilots ready for production. And it’s a fruitful way to test the very limits of LLMs, exploring and expanding the feasibility of industry’s ever-increasing AI ambitions.

To dive further into our value-oriented perspective, read these articles by MLW Founder Eric Siegel:

Come to HYBRID AI 2026 to turn AI’s potential into realized value – by discovering best practices that make AI products robust and deployment-worthy.

HYBRID AI 2026 is brought to you by Machine Learning Week (formerly Predictive Analytics World), AI’s business operationalization conference. When it launched in 2009, MLW set the standard for ML conferences. Its speakers include quickly-rising rockstar practitioners, as well as seasoned professionals who have been deploying ML for two or three decades.

Speaker submission instructions

To apply to speak, 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 MLW’s Hybrid AI 2026 to share how genAI, predictive analytics, and machine learning deliver a business 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.

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?

Restriction for Analytics Software Vendors

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 How-To – with concrete examples

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.

Present on proven 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 AI deployment. Either way, we encourage you to consider including your concrete deployed results within your presentation. MLW emphasizes deployment results as an important way to more fully demonstrate end-to-end evidence of a method’s value.

Evaluation – how well did it work? Proposals will be given highest consideration if specific measurements of deployment performance are included, especially when measured in comparison to a control group.

Speaker Agreement

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 commitment to be taken seriously.

Call for Speakers form

Hints & Tips for Writing Your Speaker Submission

Imagine yourself as a conference attendee. What kind of insights and how-to’s would interest you most? What excites you? What kind of session description draws you to attend that session? Consider those questions carefully when you write your session title and description.

For starters two quick rules: Avoid sounding commercial (“salesy”), and don’t overly rely on buzzwords.

Say enough, but don’t say too much. One sentence – or a list of bullets – is not enough. More than 100 words is usually too much. Express your message fully but succinctly.