H2O.ai has developed a rich ecosystem of MLOps and model management capabilities to not only get models into production faster, but also to keep them there. H2O AI Hybrid Cloud offers complete capabilities to deploy, monitor, test, explain, challenge, and experiment with real-time models in production.
H2O’s MLOps technology enables users to watch in real-time how data and predictions are changing as well as monitor alerts and risk flags as they occur. H2O MLOps gives IT operations teams the tools to seamlessly update models in production, troubleshoot models, and run A/B tests in a test or live production environments.
For models in production, multiple levels of monitoring is critical. Accuracy monitoring enables operators to know if their model is getting more or less accurate over time since it was put into production. Drift monitoring allows users to see how widely or quickly the distribution of the data being fed into the model is changing, ultimately affecting the models ability to perform or understand the problem. Finally, service and latency monitoring enable the user to know if the architectures and infrastructures are performing as intended at the speeds required for this production model.
H2O AI Hybrid Cloud includes everything an operations team needs to govern models in production, including a model repository with complete version control and management, access control, and logging for legal and regulatory compliance.
Production-Ready Scoring Pipelines
H2O.ai MOJOs are extremely low latency and ideal for large scale deployment in low memory computing environments, both in batch and real-time. In H2O AI Hybrid Cloud, MOJOs are automatically deployed into containers and run on Kubernetes.
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H2O Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in just minutes or hours, not months.