Accelerate Your AI Strategy
H2O Driverless AI employs the techniques of expert data scientists in an easy to use application that helps scale your data science efforts. Driverless AI empowers data scientists to work on projects faster using automation and state-of-the-art
computing power from GPUs to accomplish tasks in minutes that used to take months.
With Driverless AI, everyone including expert and junior data scientists, domain scientists, and data engineers can develop trusted machine learning models. This next-generation automatic machine learning platform delivers unique and advanced functionality for data visualization, feature engineering, model interpretability and low-latency deployment.
Key Features of H2O Driverless AI
- Automatic Feature Engineering
- Machine Learning Interpretability (MLI)
- NLP with TensorFlow
- Automatic Scoring Pipelines
- Time Series
- Automatic Visualization
- Flexibility of data and deployment
- NVIDIA GPU Acceleration
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.
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.
Text data can contain critical information to inform better predictions. Driverless AI automatically converts short text strings into features using powerful techniques like TFIDF. 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.
Automatically generates both Python scoring pipelines and new ultra-low latency automatic scoring pipelines. The new automatic scoring pipeline is a unique technology that deploys all feature engineering and the winning machine learning model in a highly optimized, low latency production ready Java code that can be deployed anywhere.
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.
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.
H2O Driveless AI works across a variety of data sets including Hadoop HDFS, Amazon S3, and more. H2O Driverless AI can be deployed everywhere including all clouds (Microsoft Azure, AWS, Google Cloud) and on premises on any system, but it is ideally suited for systems with GPUs including IBM Power 9 with GPUs built-in.
H2O Driverless AI is optimized to take advantage of GPU acceleration to achieve up to 40X speedups for automatic machine learning. Including multi-GPU algorithms for XGBoost, GLM, K-Means and more. GPUs allow for thousands of iterations of model features and optimizations.
How it Works
Accelerate Machine Learning Projects to Gain Business Insights
H2O Driverless AI makes it easy to apply advanced machine learning to solve today’s business problems.
Survival of the Fittest
Use high performance computing to create and test thousands of model iterations in minutes.
Avoid Common Data Science Pitfalls
Use the best practices from expert data scientists to avoid pitfalls like data leakage, overfitting and more.
Featured Use Cases
Providing predictive insights to decision makers and frontline employees is critical to improving customer satisfaction and decreasing operating costs across industries.
Detecting fraud even before it happens can prevent significant losses for financial institutions and prevent headaches for customers that can damage relationships.
Finding ways to improve the claims process can save money but also makes sure that customers and patients with legitimate issues are taken care of.
Related Case Studies
Dr. Robert Coop
Artificial Intelligence and Machine Learning Manager, Stanley Black & Decker
"The platform’s feature engineering and scoring pipeline generation are better than anything we’ve seen out there right now."Learn More
Chief Product Officer, G5
"AI to do AI is absolutely a watershed moment in our industry."Learn More