End-to-End MLOps for Recommender Systems
Empowering builders to deliver and streamline the lifecycle of recommender systems with a single platform to drive your models through pipeline and maintain it continuously.
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Custom recommendation model training
Leverage Qwak's platform to train personalized recommendation models with ease. Customize any model to your own needs and deploy either real-time or batch models at unparalleled speed.
Seamless integration with Feature Store
Use features from Qwak's feature store for model training and inference. Efficiently reuse features across different models, ensuring consistency and saving valuable development time.
Scalable model deployment
Deploy your recommendation engines at any scale, with simple auto-scaling policies. Whether you're serving a few users or millions, Qwak ensures that your recommender system scales efficiently with your user case, maintaining high performance and reliability.
Monitor and Optimize your recommender systems
Keep your recommender systems at peak performance with Qwak's continuous monitoring tools. Track key metrics, identify issues early, and iterate quickly to ensure your recommendation models stay relevant and effective.
Automate model lifecycle
Streamline your ML workflows with Qwak's automation capabilities. Our Platform automates each step, from data ingestion to model deployment, ensuring efficiency, consistency and accuracy. Reduce manual effort and focus on innovation and optimization.
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.
Don’t just take our word for it
Qwak was brought onboard to enhance Lightricks' existing machine learning operations. Their MLOps was originally concentrated around image analysis with a focus on enabling fast delivery of complex tabular models.
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