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: From Alphabet’s X on AI to Innovate Architecture
Date:
Tuesday, May 5, 2026
Time:
1:15 pm
Summary:
More information about this keynote address is coming soon — about Anori, X’s moonshot to make building and development faster, more affordable and more efficient.
Speaker/s:
Henry Castellanos, American ExpressKeynote: GenAI’s Killer App: Validating Predictive AI
Date:
Wednesday, May 6, 2026
Time:
8:45 am
Summary:
Henry Castellanos has lived in the analytics trenches across a wide range of enterprises. He believes that realizing value with predictive AI is almost always an extreme challenge that’s met only with special attention to robustness, scale, and explicit KPIs.
In recent years, Henry has found that genAI serves a crucial role in this. Rather than usurping analytics projects — which have only grown more important — genAI empowers practitioners to develop a new degree of robustness and validation for all things quantitative, from anomaly detection to predictive AI.
Come to Henry’s keynote address to hear his unparalleled story and lessons-learned.
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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