The Verta Enterprise MLOps (ML Operations) Platform takes any data science or ML model and instantaneously packages and delivers it by using best-in-class DevOps support for CI/CD, operations, and monitoring. It helps to automate increased availability, scaling, and safe deploys to simplify your workflow and get more out of your AI-ML data.
The platform offers 4 key modules for all your MLOps needs.
Verta Experiment Management helps you build high-quality models and release them to production faster, using state-of-the-art experiment tracking, model reproducibility, dataset versioning, and model meta-data visualization capabilities. Ensure model reproducibility and quality from experiment to production.
The Verta Model Registry is a place to find, publish, and use ML models (or model pipeline components.) Similar to container registries like DockerHub or Python package registries like PyPi, builders of models and ML pipelines can publish ready-to-consume components to the Verta Model Registry.
Verta Model Deployment lets you package, deploy, and safely release model versions using CI/CD best practices. Once your models are safely in production the platform offers elastic scaling, high availability, and reliability to serve diverse workloads and operate at scale.
With Verta Model Monitoring, get real-time insights and alerts on model performance and data characteristics, debug anomalies and initiate proactive actions. We believe your needs and every model is unique so we built a fully configurable monitoring framework to scale for any use case and serving infrastructure.