Model Registry

Take your models from research to production with a centralized and production ready model registry.

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Model Registry

About Model Registry

Qwak Model Registry accelerates the journey from research to production by providing a centralized platform for secure model storage and seamless deployment, fostering efficient collaboration and iteration for data science teams.

Version Management

Automate versioning of ML models, data, code and parameters and easily compare model versions.

Standard Structure

Use a single and flexible standard format for all your ML projects.

Remote Model Builds

Build models on a scalable infrastructure for optimized efficiency and performance.

Use Cases

Manage Model Lifecycle

The Qwak Model Registry allows collaborative work among data scientists and ML Engineers, enabling them to share models seamlessly. With a centralized registry, team members can use a single Machine learning model database for the entire ML needs.

Manage Model Lifecycle
CI/CD Integration

CI/CD Integration

Automate model build processes for continuously evaluating models, ensuring your production models are up-to-date.

Experiment Tracking

Accelerate model iterations with our central ML model storage solutions, gaining visibility into training parameters, parameter tuning and complete Machine learning metadata storage.

Experiment Tracking
Model Health Monitoring

Model Health Monitoring

The Qwak Model Registry can be integrated with monitoring tools to track the health and performance of models in real-time. This provides valuable insights into when a model might be degrading or not performing as expected.

Success stories using Model Registry

From our very first interaction, it was clear that Qwak understood our needs and requirements. Their platform enabled us to deploy a complex recommendations solution within a remarkably short timeframe. Moreover, Qwak is an exceptionally responsive partner, continually refining their solution.


We ditched our in-house platform for Qwak. I wish we had found them sooner.


Qwak streamlines AI development from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value.


Model Registry Features

Model Performance Tracking

Tools for tracking the performance of models over time, helping to identify when models need to be retrained or updated.

Automated Model Evaluation

Capabilities for automatically evaluating models to ensure they meet certain performance criteria before being moved into production.

Integration with ML Workflows

Compatibility with existing machine learning workflows and tools, ensuring that the model registry can be easily integrated into the current MLOps pipeline.

Model Collaboration & Sharing

Features that enable seamless collaboration among data scientists and ML engineers, allowing for easy sharing and discussion of models within the team.

Version Control

Ability to track and manage different versions of models, making it easier to revert to previous versions if needed and to understand the evolution of each model.

Centrlalized Model Storage

A single repository for storing and managing all machine learning models, facilitating easy access and organization.

Chat with us to see the platform live and discover how we can help simplify your AI/ML journey.

say goodbe to complex mlops with Qwak