InferenceModel Deployment

Qwak Model Deployment removes the barriers between data science and engineering teams. Deploy scalable models to production with just one click!

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Model Deployment Overview

Qwak Model Deployment enables teams to deliver an endless number of machine learning use cases in a fast, repeatable, and scalable manner, complete with advanced metrics, logging, and alerting capabilities.

Main Benefits

One click deployment

Easily deploy models using the Qwak UI, CLI, or SDK.

Auto scaling

Qwak Model Deployment automatically scales deployed models based on predefined metrics.


Easily track the metrics, logs, and performance of your models in one place with Qwak Model Deployment.

Getting Started

Deploy a Qwak build using any of the mentioned modes using Qwak CLI, management application, or Python SDK.
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Qwak Model Deployment Use Cases

Batch inference

Deploy your models as batch inference jobs when you need to generate many predictions at once using scalable compute resources.

Real-time inference

Use Qwak Model Deployment to deploy your models as real-time endpoints and generate predictions on a single observation at runtime.

Streaming inference

Use Qwak Model Deployment to deploy your model as a streaming application and trigger it in an asynchronous manner or on an existing stream of data.

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