H2O Driverless AI
Award-winning Automatic Machine Learning (AutoML) Platform
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. Driverless AI now also includes state-of-the-art PyTorch BERT transformers. With advanced NLP techniques, 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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Image Processing
H2O Driverless AI delivers state-of-the-art image processing capabilities using over 30 pre-trained image transformers and models including (SE)-ResNe(X)ts, DenseNets, MobileNets, EffientNets, and Inceptions. Images can be processed alone or as part of larger datasets that include tabular, text, and image data on CPUs or GPUs. Deliver computer vision and visual AI projects faster with automatic testing across all the leading techniques to find the best model with H2O Driverless AI.
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Time Series
H2O Driverless AI delivers superior time-series capabilities to create forecasts. Driverless AI now includes SIERD for epidemic response so that customers can add COVID-19 models to their forecasts. With Driverless AI, users can forecast 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 Resources & Blogs
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H2O Driverless AI
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Make your own AI — Add Your Game to Auto-ML Models
When Features and Algorithms compete, your Business Use Case(s) wins! …
Read BlogFeatured Use Cases
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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