H2O.ai provides a comprehensive suite of capabilities surrounding machine learning operations that support data scientists and machine learning engineers in the deployment, management and monitoring of their models in production. Additionally, the H2O AI Cloud provides an incredibly flexible architecture with distributed processing, optimized compute efficiency and the ability to deploy in the environment of your choice. Customization is well supported with easy integration of your own transformers, recipes and models.
Machine Learning Operations
Operate with AI that moves you from idea to impact.
Machine Learning Operations:
Monitor models in real-time and set custom thresholds to receive alerts on prediction accuracy and data drift and guarantee deployed models are operating as intended.
Create a central place to host and manage all experiments. Maintain a view of all deployed versions with complete, integrated model management capabilities that are accessible by both an easy-to-use web interface and an API. You can also manage models trained on any 3rd party framework.
You can build once and deploy to any scoring environment with target deployments. Deploy in different modes, including multi-variant (A/B), champion, challenger and canary. Models can be scored in real-time, in batch, asynchronously or as streaming data.
Maintain model oversight and know when data drift occurs. Feature importance delivers local explanations as to which features are contributing the most or least to prediction values. You can set custom thresholds to receive alerts and notifications for all monitored metrics.
The H2O AI Cloud is environment agnostic so any company, regardless of their existing infrastructure, can incorporate H2O.ai technologies into their machine learning pipelines.
The H2O AI Cloud is platform agnostic with clients for Python, R and Java. Users benefit from the latest versions of all major open source packages and gain control over them with our built-in custom recipe architecture. You can train, deploy and customize both H2O.ai and third party models.
Distributed machine learning backends can handle any data size by scaling out to multiple worker nodes, with model training occurring across multiple CPUs and GPUs. Cloud resource allocations are handled automatically with a kubernetes-based deployment approach.
Easily scale workloads with support for the unprecedented compute and network acceleration of Ampere-based NVIDIA GPUs and the use of the latest CUDA runtime. High performance computing is delivered through full NVIDIA RAPIDS integration.
The H2O AI Cloud makes it easy for data scientists to quickly and seamlessly hand over their models to machine learning engineers. This allows data scientists to focus on discovering new insights in additional data sources, increasing the accuracy and performance of machine learning models and driving further systematic innovation efforts.
Machine Learning Engineers
H2O.ai makes deployment easy with real-time, customizable monitoring and alert systems. The H2O AI Cloud offers a multitude of capabilities for backtesting, challenging and validating your models over time. Easily incorporate multiple ongoing Responsible AI and fairness metrics into your ongoing monitoring programs.
The H2O AI Cloud simplifies the provisioning of software for all parts of the data science lifecycle, from data access all the way through to AI application deployment. Self-service is enabled through a centralized deployment environment. Resource monitoring and cost controls allow IT professionals to optimally balance cost and performance.