2026 KEYNOTES
Speaker/s:
Dr. Eric Siegel, Machine Learning WeekKeynote: Predictive AI’s New Killer App: GenAI’s Reliability Layer
Date:
Tuesday, May 5, 2026
Time:
8:20 am
Summary:
The most ideal way to soften the AI bubble’s looming detonation would be to boost AI’s realized value. How? A new reliability layer that tames large language models.
Enter predictive AI. LLM-based projects are usually too unreliable to move from pilot to production, but predictive AI can realize genAI’s bold, often overzealous promise of autonomy – or at least a great deal of it.
Join MLW conference founder Eric Siegel to learn why predictive AI is as crucial as ever in this “genAI world,” especially since it is positioned to solve genAI’s deadly reliability problem.
Speaker/s:
Kirk Mettler, IBMKeynote: The AI-Value Sweet Spot: Blending LLMs with Enterprise ML
Date:
Tuesday, May 5, 2026
Time:
8:45 am
Summary:
IBM Chief Data Scientist Kirk Mettler is one of those seasoned analytics veterans who don’t buy most of the AI hype – but still see a vital role for LLMs. His perspective plays out fortuitously as he dives into the weeds of advanced projects, even while he manages a large team of data scientists, data engineers, and other data professionals. “Today’s sky-rocketing investment into LLMs is just plain inappropriate,” Mettler says, “And yet, not leveraging LLMs to augment predictive AI projects would be just as inappropriate! We hit the sweet spot by blending LLMs with the time-honored tradition of enterprise machine learning.”
Conference Founder Eric Siegel will interview Mettler, digging in for what promises to be an extremely enlightening fireside keynote chat.
Speaker/s:
Julia Ling, X, the moonshot factoryKeynote on Generative Architecture: An Imperative for AI Validation in the Architecture, Engineering, and Construction Industry
Date:
Tuesday, May 5, 2026
Time:
1:15 pm
Summary:
The adoption of AI in technical fields is fueling a need for more rigorous, more modular, and more domain-specific validation frameworks. At Anori, Alphabet X’s moonshot for housing and development, we are applying multimodal generative AI to make building and development dramatically faster, less costly and more efficient. The architecture, engineering, and construction industry suffers from entrenched inefficiency due to the highly constrained, highly regulated, and regionally-specific nature of building development that currently relies on numerous domain experts to sequentially flesh out a design.
Anori is creating a unified AI platform to change the paradigm and make it possible to design for many of these constraints simultaneously. Importantly, building design requires more than just beautiful images and eloquent descriptions: it requires engineering precision, regulatory compliance, and functional performance. In this setting, it is critical to have rigorous frameworks for evaluating model outputs at each step of the process, including both automated validation and human feedback. In this talk, we’ll discuss frameworks to bring transparency, diagnosability, and specificity to the model validation process.
Speaker/s:
Emre Okcular, OpenAIKeynote: Context Engineering: How Machines Remember and Forget
Date:
Wednesday, May 6, 2026
Time:
8:45 am
Summary:
The ‘magic moment’ for AI agents is hidden in the memory layer. Context Engineering is the art of shaping what an AI model knows at any moment by managing how information enters, persists, or fades from its working memory. In this session, we explore how machine learning systems “remember” through state objects, notes, and retrieval—and how they manage context using compression, selection, and context limits. We’ll walk through real-world agent patterns that balance personalization with privacy, performance, and relevance. Participants will learn practical techniques to design memory that feels intentional, evolving, and human-aware.
Speaker/s:
Jon Francis, State FarmKeynote: AI Transformation Hinges on Very Particular Organizational Requirements
Date:
Wednesday, May 6, 2026
Time:
1:15 pm
Summary:
With an extensive record serving executive roles at the likes of Starbucks, GM, and State Farm, Jon Francis has learned what makes the difference between scalable AI systems and those that never get beyond pilots or even just ideation. “Here’s a hint,” he says. “At most companies, it has almost nothing to do with the math or technology, but instead hinges on culture, transformation, and change management.”
Join this fireside-chat keynote, where Machine Learning Week founder Eric Siegel will interview Francis and dive in.
Why HYBRID AI 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.
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.
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.
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Need more information? / FAQS
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The history of Machine Learning Week
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. 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. All are now together as Machine Learning Week. In 2026 ML Week is running HYBRID AI in San Francisco, back where it all started.
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What is predictive AI?
Predictive AI (aka predictive analytics) is the application of enterprise machine learning to improve operations, such as optimizing 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 — analytics just doesn’t get more actionable than that.
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Is predictive AI different from forecasting?
Machine Learning Week events often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive AI is used as a component to build a forecast model.
However, predictive AI 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 AI tells you which individual customers are likely to buy an ice cream cone.
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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.
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Is this a “big data” conference?
Yes. Predictive AI 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.
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Is this an AI conference?
Yes. Artificial intelligence (AI) is a broadterm with many possible definitions—but by any definition, it always includes machine learning (predictive modeling) as an example of AI technology/capabilities.
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Is Machine Learning Week run by a software vendor?
No. Machine Learning Week provides a balanced view of predictive AI methods and tools across software vendors and solution providers.
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Is Machine Learning Week a research conference?
No. Machine Learning Week is focused on today’s commercial deployment of predictive AI, rather than academic or R&D activities. Separately, there are a number of research-oriented conferences; in predictive AI’s commercial application, we are essentially standing on the shoulders of those giants known as researchers.
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