Custom search & ranking model training

Streamline the model training of custom search & ranking models with the Qwak platform. Use the data processing capabilities to create models that accurately reflect user preferences and behavior patterns.

Streamline the model training of custom search & ranking models with the Qwak platform. Use the data processing capabilities to create models that accurately reflect user preferences and behavior patterns.
Improve search with embedding models

Improve search with embedding models

Harness the power of embedding models to transform complex data into meaningful representations. Our platform supports seamless integration and optimization of embedding models, essential for effective search and ranking tasks.

Scalable model deployment

Deploy your search and ranking models effortlessly at scale. Qwak's robust infrastructure ensures high availability and low latency, adapting dynamically to fluctuating workloads and user demands.

Scalable model deployment
Seamlessly monitor your ranking models

Seamlessly monitor your ranking models

Keep a close eye on your ranking models' performance with Qwak's monitoring tools. Track accuracy, response times, and user engagement in real-time to continuously refine and improve model effectiveness.

Qwak GPU Cloud

Achieve top model performance and save costs with effortless GPU autoscaling and simple model deployment on any of the various GPUs available on the Qwak GPU cloud.

Qwak GPU cloud pricing

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Don’t just take our word for it

In the rapidly evolving landscape of property management technology, optimizing data processes remains paramount. Guesty, a leading player in this domain, faced challenges in streamlining its data science operations and hastening model deployment. This case study delves into Guesty's unique challenges and highlights how a strategic partnership with Qwak provided innovative solutions.

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Before Qwak we had no standard methodology for productionizing AI models. There was no alignment between the data science and engineering teams and we spent weeks analyzing code and solving errors. Basically, it was a huge mess

Expert talks about LLMs

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