Product Overview

Qwak Serving allows deployment of scalable models to production with one click, which reduces the friction between data science and engineers.

Qwak Serving enables teams to deliver prediction services in a fast, repeatable, and scalable way—including advanced metrics, logging, and alerting capabilities.

The Main Pillars

One click deployment

Easily deploy models using UI, CLI, or Qwak SDK

Auto scaling

Automatically scales deployed models based on predefined metrics.

Observability

Track your models’ metrics, logs, and performance in one place

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 Serving Use Cases

Batch inference

Deploy your models as batch inference jobs if you need to take advantage of scalable compute resources to generate many predictions at once.

Realtime inference

Deploy your models as real-time endpoints to generate predictions on a single observation at run time.

Streaming inference

Deploy your model as a streaming application if you need to trigger your models in an asynchronous way or on an existing stream of data.

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No commitments. No risk.
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