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.

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

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.

Automate model lifecycle
Scalable Multi-Model Deployment & A/B Testing

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.

Qwak GPU cloud pricing

Don't be late in the game.

AI apps need flexible infra, stay ahead or fall behind.

Start for Free

More on Fraud Detection

No items found.

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.

Read case study

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

Expert talks about LLMs

Explore additional solutions