ML development and deployment is siloed with brittle workflows and slow handoffs. Put all MLOps capabilities in one place with centralized management, governance, and support reducing the time to market for your ML products.
Make your models reproducible and create a central model repository with ModelDB. Collect metadata, usage metrics, and audit logs to provide governance for models. Manage your experiments, create reusable dashboards and communicate your work via reports.
Remove the need to re-implement your models when moving from research to production. Use an extensible packing and release system with integrations into existing CI/CD platforms to enable releasing new model versions safely and rapidly.
Deployed models are like other services; except that they are not. Monitor your model inputs, outputs, and intermediates like you monitor CPU or memory. Set custom triggers, alerts, and remediations.
Ensure that models can scale to required query throughput and meet SLAs. Use only as many cloud resources as you need and keep cloud costs low. Apply ML-specific workload optimizations and make the most of your compute resources.