Make machine learning models and AI applications with accuracy, speed and transparency.
H2O.ai built AI to do AI. With industry-leading automated machine learning (autoML), the H2O AI Cloud gives users more accuracy, speed, and transparency throughout the entire machine learning lifecycle, including the development and deployment of AI applications.
White Box Models
Third Party Models
Low Code App Development
Low Code UI Creation
Make AI that moves you from idea to impact.
Automatically visualize and address data quality issues with advanced feature engineering that transforms your data into an optimal modeling dataset.
Easily surface interesting statistical properties and expose data quality issues with data visualization and automated data insights. Produce higher accuracy and better generalization of machine learning models with pre-processing transformers, dataset splitting, missing value handling, and outlier detection.
Use domain knowledge to select relevant data, create features, transform them into additional features and then select the best ones to optimize model performance. Increase accuracy with automated feature engineering, feature encoding, feature transformation recipes, per feature controls, and automated validation and cross-validation.
Easily manage and provision access to curated features that ensure quality and consistency in the building of machine learning models across your organization. Feature Store components include data pipeline and integrations, categorization, search, governance and access management.
Quickly create and test highly accurate and robust models with state-of-the-art automated machine learning that spans the entire data science lifecycle and can process a variety of data types within a single dataset.
Automated Machine Learning (autoML)
AutoML is pervasive across the entire H2O AI Cloud. Powering everything from feature transformation to model selection, monitoring and deployment, robust autoML capabilities are the engine behind our ability to deliver AI that does AI.
Time Series Forecasting
Easily fit and solve forecasting problems with unique feature engineering and autoML capabilities specifically designed to handle time series data. See how predictions are generated, and easily build forecasts across many categories such as individual skus, product hierarchy and more.
Natural Language Processing (NLP)
Extract insights from unstructured text, to discover trends, create more accurate and relevant information retrieval and make personalized recommendations. For common problem types such as, text classification and regression, token classification, span prediction, sequence to sequence learning, and metric learning, make state-of-the-art models without having to write code. For other problem types, use a wide range of supported algorithms from simplistic TF-IDF based ones to state-of-the-art BERT based transformers.
Computer vision use cases span across in-store customer service analysis and customer route predictions to manufacturing quality assurance and predictive maintenance. For common problems types such as image classification/regression, object detection, semantic segmentation, instance segmentation, and metric learning, make computer vision models without having to write code. Combine additional data types with your unstructured data including text, tabular and audio data. Get out-of-the-box access to all of the recent CNN-architectures and GPU accelerated training.
Quickly understand and manage large amounts of unstructured data to make accurate AI models that classify documents, extract texts and images, and refine extracted information to process documents that will increase productivity and find hidden insights.
Easily understand the ‘why’ behind model predictions to build better models and provide explanations of model output at a global level (across a set of predictions) or at a local level (for an individual prediction).
White Box Models
Competition winning modeling methods simultaneously enable model transparency and robust post-hoc interpretability methods for explaining and understanding your models. Additionally, H2O.ai’s autoML provides virtually endless constraints and parameter controls to ensure your model is as simple or as complex as you need it to be.
The H2O AI Cloud comes with one of the most robust and dynamic explainable AI toolkits that enables customers to leverage dozens of post-hoc explanation methods and understand why your machine learning model came to the decisions that it did.
One of the largest concerns for companies adopting machine learning models is the possibility of having the models perpetuate bias from the given dataset. Built from our leading research on fairness in AI, we provide multiple methods to identify, explain and debug bias in machine learning models.
Third Party Models
The H2O AI Cloud allows you to both bring your own model and bring your own recipe to explain those models. This allows users to take advantage of our comprehensive platform capabilities, while simultaneously offering the flexibility to build or bring your own explainability methods.
Low Code Application Development Framework:
Rapidly build AI prototypes and applications with a low code framework (Python/R) that makes it easy to deliver innovative solutions by seamlessly integrating backend machine learning capabilities with front end user experiences.
Reduce the time it takes to build applications with access to data science best practices and a library of functional prototypes spanning multiple industries. Simplify the management of solution development with the ability to see UI changes in real time.
Low Code User Interface Creation
Easily develop consumer facing, interactive applications with a Python/R framework that supports integrated UI and AI development. Significantly reduce the time and effort to build web applications and preview it live as you code.
Machine Learning Integration
API access to best-in-class data science capabilities makes it easy to integrate machine learning functionality into new or existing solutions. Our real-time application server enables developers to stream on-going changes to dashboards, model results and any other dynamic information in the application.
With extensive automation and transparency throughout the machine learning lifecycle, the H2O AI Cloud augments data science teams with the accuracy, speed and confidence required to produce trustworthy, fair and reliable models, deploy them quickly and maintain effective oversight of model performance.
Simplifying the hand-off between data scientists and developers, the H2O AI Cloud makes it easy to build prototypes and applications with a low code framework. The integration of complex machine learning pipelines and an intuitive front end experience means AI solutions are fielded quickly.