Customize and deploy open source AI models, create your own digital assistants and business GPTs.
Open weight small vision-language models for OCR and Document AI.
Industry and Use Case AI Apps
From Credit Scoring and Customer Churn to Anti-Money Laundering
From Clinical Workflow to Predicting ICU Transfers
From Claims Management to Fraud Mitigation
From Predictive Maintenance to Transportation Optimization
From Content Personalization to Lead Scoring
From Assortment Optimization to Pricing Optimization
From Predictive Customer Support to Predictive Fleet Maintenance
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Read the H2O.ai wiki for up-to-date resources about artificial intelligence and machine learning.
Learn the best practices for building responsible AI models and applications
By H2O.ai Team | minute read | May 10, 2013
In this double header we present a practitioners close view of the science and an engineer’s close view of design and implementation of distributed algorithm.
Day in the Life of a Data Scientist – Chris Pouliot
In this session, Netflix
analytical leader Chris Pouliot shares his experience building a large team of data scientists at Netflix and what a typical day in life of a Data Scientist looks like. From extracting and exploring data to posing good questions around them and matching them with the right algorithms, Chris goes through the lifecycle of data science in practice.
Chris built a central, horizontal team for the company that spans across all business verticals. Chris shares insights and stories, covering pitfalls and successes and impact they have at Netflix.
Distributed Generalized Linear Modeling (GLM) –
Tomas Nykodym
In this session, 0xdata engineers, Tomas Nykodym & Cliff Click explain how to build a Distributed GLM (Logistic, Poisson Regression .) Generalized_linear_model is the most popular tool at the hand of a good datascientist. A couple of very powerful mathematical approaches such as Stephen Boyd’s ADMM and Generalized Gradients are analyzed along with implementation choices. Live Demo and performance comparisons between the two approaches and on applications on Big Data will be presented.
At H2O.ai, democratizing AI isn’t just an idea. It’s a movement. And that means that it requires action. We started out as a group of like minded individuals in the open source community, collectively driven by the idea that there should be freedom around the creation and use of AI.
Today we have evolved into a global company built by people from a variety of different backgrounds and skill sets, all driven to be part of something greater than ourselves. Our partnerships now extend beyond the open-source community to include business customers, academia, and non-profit organizations.
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