Model Registry
Take your models from research to production with a centralized and production ready model registry.
Get Started
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.
Automate versioning of ML models, data, code and parameters and easily compare model versions.
Use a single and flexible standard format for all your ML projects.
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.
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.
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.
Tools for tracking the performance of models over time, helping to identify when models need to be retrained or updated.
Capabilities for automatically evaluating models to ensure they meet certain performance criteria before being moved into production.
Compatibility with existing machine learning workflows and tools, ensuring that the model registry can be easily integrated into the current MLOps pipeline.
Features that enable seamless collaboration among data scientists and ML engineers, allowing for easy sharing and discussion of models within the team.
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.
A single repository for storing and managing all machine learning models, facilitating easy access and organization.
Say goodbye to complex AI/ML
Chat with us to see the platform live and discover how we can help simplify your AI/ML journey.
