Register & Train ModelsModel RegistryModel Registry
Centralize your models in a production ready registry and automate versioning of data, code and parameters for every ML build. Compare model versions to identify and emphasize the best production candidate.
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Model Registry Overview
Qwak Model Registry brings the benefits of traditional build processes to machine learning (ML) projects, enabling data scientists to create immutable, tested artifacts for production.
Qwak Model Registry standardizes an ML project structure that automatically versions data, code, and parameters for every model build.
Qwak Model Registry standardizes an ML project structure that automatically versions data, code, and parameters for every model build.
The Main Pillars
Version management
Builds with different configurations can be created and compared, and build data can be queried and visualized.
Standardization
The Qwak Model Registry offers a single, flexible structure for ML projects
Remote build
Create model versions on remote, elastic resources using the Qwak Model Registry Each build can be configured with different parameters, data sources, and resources.
Getting Started
Easily create new models with Qwak SDKs and run a new build using the Qwak CLI. For more information, check out the
Start for freeBuilds Use Cases
Reproduce past builds
The Qwak Model Registry generates deployable artifacts that can be reused and deployed as needed. In addition, it enables data scientists to understand and reproduce the process of creating a build when necessary.
Model version tracking
ML models can involve multiple variables, such as the data they were trained on, configured hyperparameters, and even different source code. The Qwak Model Registry allows you to track and compare the differences between multiple builds, and to analyze your build data, code, and parameters.
One workflow that fits all
The Qwak Model Registry is designed to support any type of model and data, and helps organizations establish development standards and create a robust process for tackling ML challenges.
