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.
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.
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.Learn More
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.Learn More
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.Learn More
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.Learn More
Other Capabilities of H2O Driverless AI
- Time Series
- Automatic Visualization
- Flexibility of data and deployment
- NVIDIA GPU Acceleration
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.Watch Video
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.Watch Video
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.
H2O Driverless AI is optimized 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. GPUs allow for thousands of iterations of model features and optimizations.
Driverless AI Architecture
H2O Driverless AI
How It Works
- 1. Ingest Data - Many Sources
- 2. Understand your Data
- 3. Build Predictions Automatically
- 4. Explain and Audit Predictions
- 5. Package and Deploy Scoring Pipelines
Bring data in from cloud, big data and desktop systems.
Understand the data shape, outliers, missing values, etc.
Use best practice model recipes and the power of high performance computing to Iterate across thousands of possible models including advanced feature engineering, model tuning and model stacking.
Automatically build human readable dashboards to understand complex prediction models.
Deploy ultra-low latency Python or Java Automatic Scoring Pipelines that include feature transformations and models.
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
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."Watch the Video
Chief Product Officer, G5
"AI to do AI is absolutely a watershed moment in our industry."Watch the Video
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."Watch the Video