Operate AI applications with agility, scale and confidence.
H2O.ai provides a comprehensive suite of capabilities surrounding machine learning operations that support data scientists, machine learning engineers, and IT operators in the management, deployment, and monitoring of their models in production. Additionally, the H2O AI Cloud provides an highly open & flexible architecture with distributed processing, optimized compute efficiency and the ability to deploy in the environment of your choice. H2O.ai has native integrations with many popular tools in the MLOps lifecycle, providing customers with greater choice. Customization is well supported with easy integration of your own transformers, recipes and models.
Machine Learning Operations
High Availability Model Hosting
Machine Learning Operations:
Use H2O MLOps to 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 across your entire organization, enabling greater sharing and collaboration. Maintain a view of all deployment versions with complete model registry and model versioning 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 natively, and/or import models stored on a 3rd party model registry.
Build once and deploy to any scoring environment with target deployments. Deploy your models in mode of your choice, including Single Model, A/B Testing, or Champion/Challenger. Your models can also be scored in real-time (synchronous or asynchronous), in batch, or as streaming data. Choose the Kubernetes configuration that is best suited for each model, including, CPU vs. GPU and min and max processing and memory allocations.
High Availability Model Hosting
Enable high availability for your deployments by selecting up to 5 replicas for your deployment. H2O MLOps will automatically check the health of each node, and load balance across nodes, so if one node fails, customers won’t experience any service interruption.
Maintain oversight for your entire deployment, including model drift, model accuracy, and deployment infrastructure. Model explanations at runtime 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. Admin users can get visibility on AI projects across the entire organization.