Overview

Accelerate Your AI Strategy

H2O Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in just minutes or hours, not months. By delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, bring your own recipe, time-series and automatic pipeline generation for model scoring, H2O Driverless AI provides companies with an extensible customizable data science platform that addresses the needs of a variety of use cases for every enterprise in every industry.

Driverless AI: The Platform to Make Your Own AI

 

AI and AutoML in a single platform

 

AI to do AI

 

Delivers insights and interpretability

 

Customize and extend with 130+ open source recipes or your domain expertise

Automatic Feature Engineering

Feature engineering is the secret weapon that advanced data scientists use to extract the most accurate results from algorithms. H2O Driverless AI employs a library of algorithms and feature transformations to automatically engineer new, high-value features for a given dataset.

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Bring Your Own Recipes

Data scientists can extend the Driverless AI platform by uploading their own models, transformers and scorers as a custom recipe. Bring-Your-Own recipes or use the examples built in the open and curated by the data science community. Driverless AI treats recipes as first-class citizens in the automatic machine learning workflow.

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Model Deployment and Operations

With H2O Driverless AI, models can be deployed automatically across a number of environment choices including creating a REST endpoint for any web applications to invoke the model, automatically run as a service in the cloud, or simply as a highly optimized Java code for edge devices.

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Machine Learning Interpretability

H2O Driverless AI provides robust interpretability of machine learning models to explain modeling results. In the MLI view H2O Driverless AI employs a host of different techniques and methodologies for interpreting and explaining the results of its models, four charts are generated automatically including: K-LIME, Shapley, Variable Importance, Decision Tree, Partial Dependence and more.

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Automatic Visualization

H2O Driverless AI automatically generates visualizations and creates data plots that are most relevant from a statistical perspective based on the most relevant data statistics to help users get a quick understanding of their data prior to starting the model building process.

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Natural Language Processing

Text data can contain critical information to inform better predictions. Driverless AI automatically converts text strings into features using powerful techniques like TFIDF, CNN, and GRU. With TensorFlow, Driverless AI can also process larger text blocks and build models using all available data and to solve business problems like sentiment analysis, document classification and content tagging.

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Time Series

H2O Driverless AI delivers superior time series capabilities to optimize for almost any prediction time window, incorporate data from numerous predictors, handle structured character data and high-cardinality categorical variables, and handle gaps in time series data and other missing values.

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Flexibility of Data Ingestion and Compute Technologies

H2O Driverless AI can ingest data from a variety of data sets including Hadoop HDFS, Amazon S3, and more. H2O Driverless AI can also be deployed everywhere including all clouds (Microsoft Azure, AWS, Google Cloud) and on-premises on any systems.

H2O Driverless AI is optimized to work with the with the latest Nvidia GPUs, IBM Power 9 and Intel x86 CPUs and to take advantage of GPU acceleration to achieve up to 30X speedups for automatic machine learning. Driverless AI includes support for GPU accelerated algorithms like XGBoost, TensorFlow, LightGBM GLM, and more.

Driverless AI Architecture

Related Case Studies

Wei Shao
Data Scientist, Hortifrut

"I am very excited to use the H2O Driverless AI because prior to it, we used to spend weeks hyperparameter tuning etc., but with Driverless AI, one experiment takes just a few hours."

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Martin Stein
Chief Product Officer, G5

"AI to do AI is absolutely a watershed moment in our industry."

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Ruben Diaz
Data Scientist, Vision Banco, Vision Banco

"The automation of the data science process reduced time and costs. And time is money. So, you can do more with the same amount of time. It's possible to deliver more value to the business, develop more use cases and focus the data science effort in the use case instead of development tasks."

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Venkatesh Ramanathan
Senior Data Scientist, Paypal

"H2O Driverless AI gives amazing performance in terms of feature performance and also model performance."

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