Key Features

The following are some of the key features available in H2O Driverless AI.

Flexibility of Data and Deployment

Driverless AI works across a variety of data sources, including Hadoop HDFS, Amazon S3, and more. Driverless AI can be deployed everywhere, including all clouds (Microsoft Azure, AWS, and Google Cloud), on-premises, and can run on machines with only CPUs or machines with CPUs and GPUs.

NVIDIA GPU Acceleration

Driverless AI is optimized to take advantage of GPU acceleration to achieve up to 40X speedups for automatic machine learning. It includes multi-GPU algorithms for XGBoost, GLM, K-Means, and more. GPUs allow for thousands of iterations of model features and optimizations and give significant speedups for use cases involving images and/or text. For more information, see GPUs in Driverless AI.

Automatic Data Visualization

For datasets, Driverless AI automatically selects data plots based on the most relevant data statistics, generates visualizations, and creates data plots that are most relevant from a statistical perspective based on the most relevant data statistics. These visualizations help users get a quick understanding of their data prior to starting the model building process. They are also useful for understanding the composition of very large datasets and for seeing trends or even possible issues, such as large numbers of missing values or significant outliers that could impact modeling results. For more information, see Visualizing Datasets.

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. (See Driverless AI Transformations for more information.) Included in the interface is a variable importance chart that shows the significance of original and newly engineered features.

Automatic Model Documentation

To explain models to business users and regulators, data scientists and data engineers must document the data, algorithms, and processes used to create machine learning models. Driverless AI provides an AutoDoc for each experiment, relieving the user from the time-consuming task of documenting and summarizing their workflow used when building machine learning models. The AutoDoc includes details about the data used, the validation schema selected, model and feature tuning, and the final model created. With this capability in Driverless AI, practitioners can focus more on drawing actionable insights from the models and save weeks or even months in development, validation, and deployment.

Driverless AI also provides a number of autodoc_ configuration options, giving users even more control over the output of the AutoDoc. (Refer to the Sample config.toml File topic for information about these configuration options.)

Click here to download and view a sample experiment report in Word format.

Time Series Forecasting

Time series forecasting is one of the biggest challenges for data scientists. These models address key use cases, including demand forecasting, infrastructure monitoring, and predictive maintenance. Driverless AI delivers superior time series capabilities to optimize for almost any prediction time window. Driverless AI incorporates data from numerous predictors, handles structured character data and high-cardinality categorical variables, and handles gaps in time series data and other missing values. For more information, see Time Series in Driverless AI.

NLP with TensorFlow and PyTorch

Text data can contain critical information to inform better predictions. Driverless AI automatically converts text strings into features using powerful techniques like TFIDF and Embeddings. With TensorFlow and PyTorch, Driverless AI can process large text blocks and build models using all the available data to solve business problems like sentiment analysis, document classification, and content tagging. The Driverless AI platform has the ability to support both standalone text and text with other columns as predictive features. For more information, see NLP in Driverless AI.

Image Processing with TensorFlow

Driverless AI can be used to gain insight from digital images. It supports the use of both standalone images and images together with other data types as predictive features. For more information, see Image Processing in Driverless AI.

Machine Learning Interpretability (MLI)

Driverless AI provides robust interpretability of machine learning models to explain modeling results in a human-readable format. In the MLI view, Driverless AI employs a host of different techniques and methodologies for interpreting and explaining the results of its models. A number of charts are generated automatically (depending on experiment type), including K-LIME, Shapley, Variable Importance, Decision Tree Surrogate, Partial Dependence, Individual Conditional Expectation, Sensitivity Analysis, NLP Tokens, NLP LOCO, and more. Additionally, you can download a CSV of LIME and Shapley reasons codes from the MLI page. For more information, see MLI Overview.

Automatic Reason Codes

In regulated industries, an explanation is often required for significant decisions relating to customers (for example, credit denial). Reason codes show the key positive and negative factors in a model’s scoring decision in a simple language. Reasons codes are also useful in other industries, such as healthcare, because they can provide insights into model decisions that can drive additional testing or investigation. For more information, see Viewing Explanations.

Custom Recipe Support

Driverless AI lets you import custom recipes for MLI algorithms, feature engineering (transformers), scorers, and configuration. You can use your custom recipes in combination with or instead of all the built-in recipes. This lets you have greater influence over the Driverless AI Automatic ML pipeline and gives you control over the optimization choices that Driverless AI makes. For more information, see Custom Recipe Management.

Automatic Scoring Pipelines

For completed experiments, Driverless AI automatically generates both Python scoring pipelines and new ultra-low-latency automatic scoring pipelines (MOJO) for deploying the model to production. The new automatic scoring pipeline is a unique technology that deploys all feature engineering and the winning machine learning model in highly optimized, low-latency, production-ready Java or C++ code that can be deployed anywhere. For more information, see Scoring Pipelines Overview.

Experiment Setup Wizard

The Driverless AI Experiment Setup Wizard makes it simple for you to set up a Driverless AI experiment and ensure that the experiment’s settings are optimally configured for your specific use case. The Experiment Setup Wizard helps you learn about your data and lets you provide information about your use case that is used to determine the experiment’s settings. For more information on the experiment setup wizard, see H2O Driverless AI Experiment Setup Wizard.