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.
Impressions of previous MLW conferences
What our attendees think!
WITNESS HOW PRACTITIONERS AT THESE LEADING ENTERPRISES (AND MORE) APPLY MACHINE LEARNING:
Partners
2025 KEYNOTES + SPECIAL PLENARY
Speaker/s:
Dr. Eric Siegel, Machine Learning WeekKeynote: Five Ways to Hybridize Predictive and Generative AI
Date:
Tuesday, June 3, 2025
Time:
8:15 am
Room:
Deer Valley
Summary:
Predictive AI and generative AI compete for attention and budgets – but they shouldn’t. They’re two distinct categories of enterprise ML use cases that solve different problems. Predictive AI improves existing large-scale operations, while genAI generates new content items and code, answers questions, serves as a thought partner, etc.
But a marriage of the two is ushering in new value propositions. For example, predictive AI promises to realize genAI’s ambitious claims of potential autonomy (removing or reducing the human in the loop). And genAI improves the use of predictive AI by helping code it, helping explain it to stakeholders, and more.
That’s only the tip of the iceberg. Before track 2’s breakout sessions covering such hybrid approaches, come to this keynote session for Machine Learning Week Founder Eric Siegel’s overview of this trend.
Keynote: Revolutionizing the Electric Grid: The Transformative Power of AI and Innovation
Date:
Tuesday, June 3, 2025
Time:
9:00 am
Room:
Deer Valley
Summary:
The electric grid is at a crossroads, needing radical modernization to meet the demands of a rapidly changing energy landscape. Join Ravi Jain, Chief Technology Officer of Tapestry, a project at X, Alphabet’s innovation lab, to explore how Tapestry’s AI-powered platform is unlocking a more efficient, resilient, and sustainable grid. When applied strategically, AI has the potential to revolutionize grid planning, operations, and management, enabling faster integration of renewables and a more reliable energy future. Learn about real-world deployments and the crucial role of collaboration in harnessing AI’s transformative power for the grid.
Machine Learning Week founder Eric Siegel has written these two articles about this Alphabet initiative’s use of machine learning for its electric grid moonshot project.
Speaker/s:
Dr. Erika McBride, PaychexKeynote: Calling all Data Geeks! How to Establish a Community of Practice!
Date:
Tuesday, June 3, 2025
Time:
1:30 pm
Room:
Deer Valley
Summary:
With the explosion of interest in AI, “Citizen Data Scientists” have emerged all across our organizations. The first reaction may be to try to squash their activities to control the risk of inaccurate data storytelling and misinformation. But a more effective strategy is to embrace these fellow data geeks, and enable them with tools and best practices to further accelerate the data and AI journey. Hear how Paychex has launched and matured the Data and AI Community of Practice, leveraging proven techniques to engage the community and drive organizational value.
Speaker/s:
Rohit Supekar, The New York TimesKeynote: Optimizing The New York Times subscription funnel using real-time causal machine learning
Date:
Wednesday, June 4, 2025
Time:
9:05 am
Room:
Deer Valley
Summary:
The New York Times (NYT) launched its paywall in March 2011, beginning its journey as a subscription-first news and lifestyle service. From its inception, this metered access service was designed so that non-subscribers could read a fixed number of articles every month. With the NYT’s technological transformation into a data-driven digital company, we now successfully develop models that intelligently gateway the most worthwhile pageviews in real time within milliseconds. This decision is made to maximize a combination of business objectives such as subscriptions, user engagement, and search visibility, while balancing the tradeoff between them.
This talk will begin with historical context on the digital transformation of The Times and its subscription funnel strategy with users moving from unregistered to registered and subscribed states. Following an introduction of the data science group, we will draw on differences between predictive and prescriptive machine learning models. We will then discuss how Randomized Control Trials (RCTs) can be designed to train causal machine learning models for prescriptive decision making. Modeling details about our real time models for registered and unregistered users will be discussed, highlighting the delicate balance of multiple objectives using Pareto optimization techniques. We will also present engineering technologies utilized at The Times to deploy these models for high throughput inference.
Speaker/s:
Dr. John Elder, Elder ResearchSpecial Plenary Session: The Twin Crisis in Science (and how to defeat them)
Date:
Wednesday, June 4, 2025
Time:
1:15 pm
Room:
Deer Valley
Summary:
The crises are:
- Most research results are false.
- Most discoveries never get implemented
(Perhaps, in light of the first, the second is less bad? 🙂 )
The shocking extent of these problems may lead to despair.
But hope should triumph as attainable solutions to the crises are explained.
Machine Learning Week — the facts:
Years
Speakers
Sessions
Companies
Conference Chair
2025 Workshops
Need more information?
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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 in Las Vegas. 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 analytics?
Predictive analytics optimizes 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 — business intelligence just doesn’t get more actionable than that.
Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.
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Is predictive analytics different from forecasting?
Machine Learning Week often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.
However, predictive analytics 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 analytics tells you which individual customers are likely to buy an ice cream cone.
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Is this a “data mining” conference?
Yes. Data mining is often used synonymously with predictive analytics, and, in any case, predictive analytics is a type of data mining.
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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 analytics 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 broad, subjective term 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 analytics 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 analytics, rather than academic or R&D activities. Separately, there are a number of research-oriented conferences; in predictive analytics’ commercial application, we are essentially standing on the shoulders of those giants known as researchers.
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Are you considering new speakers for Machine Learning Week?
For speaker information and proposal submissions, click here.