Model Catalog by Verta AI

Centralized ML model management and deployment.



January 11, 2024
Promotion and Deployment Checklists
Standardized Model Documentation
Best For
Machine Learning Engineer
AI Researcher
Data Engineer
Use Cases
Governance and Compliance
Streamlined Model Operationalization

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What is Model Catalog by Verta AI?

The Verta Model Catalog is an MLOps platform that serves as a central repository for managing and deploying production-ready models. It provides a unified inventory of model assets, allowing data scientists to register their models and associated metadata through a Python client in their Jupyter notebook. The Model Catalog automatically organizes and consolidates documentation, reproduction methods, and production checklists, making it accessible to the entire organization. With features such as full-lifecycle model management, standardized documentation, and real-time monitoring, the Model Catalog streamlines model operationalization and facilitates model sharing and governance across the enterprise. By providing a reliable and automated release process, it enables organizations to accelerate the model deployment process while ensuring compliance and adherence to Responsible AI principles.

Model Catalog by Verta AI Features

  • Full-Lifecycle Model Management

    Comprehensive management of models from experiment tracking to production registry.

  • Promotion and Deployment Checklists

    Customizable automations and checklists for seamless data scientist and engineer collaboration.

  • Standardized Model Documentation

    Consistent documentation, model schema, and API management across machine learning assets.

  • Reliable Inference and Serving

    Batch and real-time serving on any k8s infrastructure with real-time decay monitoring, logging, and alerting.

Model Catalog by Verta AI Use Cases

  • Model Sharing

    Enable teams within an organization to easily share and disseminate model assets, such as machine learning models and rule sets, created by different teams or individuals.

  • Governance and Compliance

    Facilitate governance over models by ensuring that new regulations or updates to existing regulations can be implemented on deployed models, providing a streamlined process for compliance and adherence to industry standards.

  • Streamlined Model Operationalization

    Integrate with IT systems and deployment platforms to simplify the operationalization process for models, reducing time-to-market and ensuring smooth deployment and monitoring of production-ready models.

Related Tasks

  • Model Registration

    Register and catalog models along with associated metadata for efficient organization and management.

  • Model Documentation

    Create standardized documentation for models, including methods for reproducibility and model schema, facilitating collaboration and knowledge sharing.

  • Model Version Control

    Track and manage versions of models to ensure proper model governance and the ability to revert to previous versions.

  • Model Promotion and Deployment

    Customize and automate promotion and deployment checklists to streamline the process for data scientists and engineers.

  • Model Monitoring

    Monitor model performance and decay in real-time, enabling proactive actions to maintain model relevance and performance.

  • Model Sharing

    Share model assets within an organization, allowing teams to benefit from each other's models and fostering collaboration.

  • Model Governance

    Implement governance policies and processes, ensuring compliance with regulations and industry standards for model deployment.

  • Model Operationalization

    Integrate the Model Catalog with IT systems and deployment platforms to operationalize models in a streamlined and efficient manner.

  • Data Scientist

    Data scientists can use the Model Catalog to register and manage their machine learning models throughout the entire lifecycle, from experimentation to production deployment.

  • Machine Learning Engineer

    Machine learning engineers can utilize the Model Catalog to organize and deploy production-ready models, ensuring a reliable and automated release process.

  • AI Researcher

    AI researchers can benefit from the Model Catalog by easily accessing and sharing model assets, enhancing collaboration and knowledge sharing within the research community.

  • Data Engineer

    Data engineers can leverage the Model Catalog to streamline the operationalization and integration of machine learning models into existing IT systems and deployment platforms.

  • Model Governance Manager

    Model governance managers can utilize the Model Catalog to enforce compliance with established governance policies and processes, ensuring adherence to regulations.

  • AI Team Lead

    AI team leads can rely on the Model Catalog to centralize model assets, enabling effective model discovery, integration, and governance across the entire team or organization.

  • Risk and Ethics Officer

    Risk and ethics officers can customize and configure the promotion and deployment checklists within the Model Catalog to ensure models meet ethical and risk-based requirements.

  • IT System Administrator

    IT system administrators can integrate the Model Catalog into their infrastructure to enable reliable batch and real-time inference and serving of machine learning models on any k8s infrastructure.

Model Catalog by Verta AI FAQs

What is Verta Model Catalog?

Verta Model Catalog is an MLOps platform that provides a central repository to manage and deploy production-ready models.

What are the key features of Verta Model Catalog?

Key features include full-lifecycle model management, promotion and deployment checklists, standardized model documentation, and reliable batch and real-time inference and serving.

How does Verta Model Catalog work?

Data and machine learning scientists register models and their metadata into the platform via a Python client in their Jupyter notebook.

What are the use cases of Verta Model Catalog?

Use cases include model asset sharing, governance over models, and streamlined model operationalization.

What are the benefits of a model catalog?

Benefits include centralized model storage and management, version control and tracking, and enforcement of compliance with governance policies.

What industries can benefit from a model catalog?

Industries with strict data privacy and security regulations, such as finance and healthcare, can benefit from a model catalog.

How can a model catalog help organizations speed up the model deployment process?

It reduces time-to-market and provides guardrails around governance, ensuring safety, security, and adherence to responsible AI principles.

Can Verta Model Catalog be customized by risk and ethics teams?

Yes, promotion and deployment checklists and automations can be customized and configured by risk and ethics teams.

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