Register & Train ModelsModel 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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Qwak Model reposiroty interface

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.

The Main Pillars

Version management

Builds with different configurations can be created and compared, and build data can be queried and visualized.


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
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Builds 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.

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No commitments. No risk.