Product Overview

Qwak build system adds “traditional” build processes to machine learning (ML) models and allows data scientists to build an immutable and tested production-grade artifact.

Qwak build system standardizes an ML project structure that automatically versions data, code, and parameters for every model build.

The Main Pillars

Version management

Different builds are built with different configurations. Builds can be compared, and build data can be queried and visualized.

Standardization

Qwak defines a single yet flexible structure for ML projects.

Remote build

Build a model version on remote elastic resources. Each build can run with different parameters, using different data sources, and with different 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

Builds create deployable artifacts. Built artifacts can always be deployed and reused. But in some cases, deploying the artifact isn’t enough. Qwak allows data scientists to understand how a build was created and reproduce it when needed.

Model version tracking

There are multiple variables in models. From the data models were trained on to the configured hyper parameter and even different source code.
The Qwak build system allows you to track and compare the differences between multiple builds, as well as analyze your build data, code, and parameters.

One workflow that fits all

Qwak supports any model type, on any type of data. It allows you to create development standards in your organization and create a standard robust process for your ML challenges.

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