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.