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New H2O Driverless AI Innovations Extend the Industry’s Leading Automatic Machine Learning Platform, the open source leader in AI and machine learning for the enterprise, announced the latest release of its award-winning automatic machine learning platform, H2O Driverless AI. Key new capabilities include the ground-breaking ability to create recipes to extend and customize the platform; model administration and collaboration for management and deployment of models; and new explainable AI capabilities for fairness and bias checks. also announced more than 100 open-source recipes that can be used for customization and extension of Driverless AI to empower customers to address specific nuances in their use cases and the ability to include their domain expertise in order to make their own AI. also is pleased to announce its next round of funding led by Goldman Sachs and Ping An.

“Every company needs to be an AI company. Our mission to democratize AI and empower all of our customers to be AI superpowers is one step closer with Bring Your Own Recipes in Driverless AI. Domain Experts can participate in the AI revolution that is transforming every vertical. We have created over 100 open source recipes, that are design patterns for AI and curated by our Grandmasters, Data Scientists and Domain Experts. We are excited to co-innovate with the community of customers to enhance and nurture an AI ecosystem,” said Sri Ambati, CEO and Founder, “Our makers have been innovating relentlessly to simplify the AI to help with the scarcity of talent and time, and to bring trust, and explainability. This is a quantum leap in the fast-moving AI and autoML space.”

“We are expecting a rapid adoption of AI in capital markets, as AI models started demonstrating ROI,” said Ediz Ozkaya, Head of Machine Learning Stats at Goldman Sachs. “ is at the forefront of the space with Driverless AI, which enables us to inject our domain-specific AI capability into the platform in a consistent manner while protecting in-house IP and staying compliant.”

“H2O Driverless AI speeds up machine learning by automating our data science workflow. With the new recipe capability, we can extend and customize the platform to meet our needs, such as estimating the prepayment risk of underlying loans in fixed-income assets like mortgage-backed securities,” said Chris Pham, Senior VP Data Management and Data Science at Franklin Templeton. “Driverless AI is helping us accelerate our AI journey.”

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Make Your Own AI with Recipes for Every Use Case

In the last year, Driverless AI introduced time-series and NLP recipes to meet the needs of demanding business. The new bring-your-own-recipe capability ensures data scientists now can quickly customize and extend the Driverless AI to make their own AI with customizations models, transformers, and scorers, extending the platform to meet any data science requirement. These customized recipes are then treated as first-class citizens in the automatic feature engineering process and eventually creating the winning model. With recipes domain experts now have more options to solve their data science problems and to make their own AI by enabling them to address a variety of use cases ranging from credit risk scoring, customer churn prediction, fraud detection, cyber threat prevention, sentiment analysis and more. also announced the availability of the first set of vertical specific solutions: anti-money laundering, customer 360 & malicious domain detection. In addition, customers can explore and consume over 100 open-source recipes, curated by Kaggle Grandmasters at

“We are pleased with the recipe feature added to H2O Driverless AI,” said Yan Yang, VP of Data Science at Deserve. “We can be more creative in how we evaluate and serve those new to credit with the ability to customize and extend the platform to meet our unique needs.”

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Model Operations, Administration, Collaboration

As data science practices mature, the need for model management, deployment, and monitoring across enterprises has increased. This involves bringing together different groups in an organization to work together in a seamless fashion to build, review and handoff models for deployment. Project Workspace in Driverless AI enables data scientists to collaborate on different projects, build models, tag and version them appropriately for DevOps and IT that can then deploy these models to different environments in a highly scalable and robust manner. In addition, H2O announced a new module for Model Admin that enables models that are deployed to be monitored for system health checks and also data science metrics around drift detection, model degradation, A/B testing, and provide alerts for recalibration and retraining.

Explainable AI: Fairness and Bias Checks

H2O Driverless AI provides robust interpretability of machine learning models to explain modeling results. H2O Driverless AI now has added the ability to perform disparate impact analysis to test for sociological biases in models. This new feature allows for users to analyze whether a model produces adverse outcomes for different demographic groups even if those features were not included in the original model. These checks are critical for regulated industries where demographic biases in the data can creep into models causing adverse effects on protected groups.

H2O Driverless AI provides interpretable-by-design models including linear models, monotonic gradient boosting and RuleFit. In its machine learning interpretability module, Driverless AI employs a host of different techniques and methodologies for explaining the results of its models like K-LIME, Shapley, variable importance, decision tree and partial dependence views.

“In a regulated industry such as banking, the ability to explain what any model does is an absolute requirement. Decisions made by models must not only be sound but also must be fair. The team at has tackled this with machine learning interpretability and disparate impact analysis to detect bias and fairness. by far has the most sophisticated and complete tools to address these critical requirements for data scientists today,” said Agus Sudjianto, EVP and Head of Corporate Model Risk at Wells Fargo.

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