Model Serving

Deploy models to production at any scale in just one click

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.

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.

Success stories using Model Serving

Asi Messica

VP Data Science

From our very first interaction, it was clear that Qwak understood our needs and requirements. Their platform enabled us to deploy a complex recommendations solution within a remarkably short timeframe. Moreover, Qwak is an exceptionally responsive partner, continually refining their solution.

Amit Segev

R&D Group Manager - Data & AI

Our AI and Machine Learning pipelines are fundamentally built on Qwak's comprehensive platform, which has been a game-changer in our journey from the initial ideation to the full-scale production of our banking chatbot 'Ella 2.0'.