Model Serving

Deploy models to production at any scale in just one click

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Model Serving

About Model Serving

One-Click Deployment

Streamline the deployment process with a single click, reducing deployment time and complexity, and ensuring that machine learning models are accessible and operational quickly.

Auto Scaling

Benefit from automatic scaling of resources based on demand, optimizing performance and cost-efficiency by dynamically allocating resources as needed to serve predictions.

Observability

Gain deep insights into model performance and behavior with robust observability features, allowing for real-time monitoring, troubleshooting, and continuous improvement of machine learning models.

Use Cases

Real-time predictions

Serve ML and AI models as live API endpoints, enabling applications as fraud detection, recommendation systems, and chatbots.

Real-time predictions
Batch processing

Batch processing

Execute bulk inference tasks on large datasets efficiently from a variety of data sources, at any scale you need. 

A/B testing & experimentation

Deploy multiple versions of a model simultaneously to evaluate and compare performance in real-world scenarios. Make data-driven decisions and continuously improve your models and applications.

A/B testing & experimentation

Success stories using Model Serving

We ditched our in-house ML platform for Qwak. I wish we had found them sooner.

Upside

Qwak streamlines ML development from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value.

Notion

People ask me how I managed to deploy so many models while onboarding a new team within a year. My answer is: Qwak.

OpenWeb

Model Serving Features

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