End-to-End MLOps for Fraud Detection
Empowering builders to deliver and streamline the lifecycle of fraud detection with a single platform to drive your models through the pipeline and maintain it continuously.
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Fraud Detection model training
Accelerate the development of fraud detection algorithms using Qwak's advanced ML tools. Leverage our managed Jupyter notebooks and comprehensive data libraries to refine and train models more effectively.
Integrate seamlessly with Feature Store
Enhance your fraud detection models by easily accessing and utilizing relevant features from Qwak's extensive Feature Store. Our platform ensures streamlined data management and feature retrieval.
Automate model lifecycle
Simplify the entire lifecycle of your fraud detection models with Qwak's automation capabilities. From development to deployment, experience effortless versioning, tracking, and maintenance.
Scalable Multi-Model Deployment & A/B Testing
Deploy multiple fraud detection models concurrently with Qwak's scalable infrastructure. Utilize A/B testing in production environments to evaluate model performance and optimize decision-making.
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
Upside relies heavily on machine learning to power their platform. Their key algorithms focus on areas like offer generation, ranking, forecasting, fraud detection, and marketing optimization. To accelerate their machine learning efforts, Upside needed an MLOps platform that could streamline model development, deployment, monitoring and collaboration between data scientists and engineers all while maintaining costs at scale.
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