Automated Machine Learning
The Future of Finance is AI-Driven
With automated machine learning, financial services companies can drive revenue growth, provide superior client experience, reduce operational costs, and improve risk management.
Automation
Enable your team to swiftly create and deploy accurate machine learning models, outpacing traditional data science methods. Build and deploy hundreds of models, selecting the best one in just hours.
Collaboration
Numerous organizations use a varied toolkit for tasks, tailored to both technical and non-technical users. Create models and generate predictions on-premises or in the cloud, utilizing either an intuitive GUI or a robust API.
Accuracy
Prioritize quality without sacrificing speed. Automatically choose the best ML algorithms, optimize data processing, and receive model deployment recommendations tailored to your data and goals.
Built for the Enterprise
Flexible delivery options
You can deploy on-premises on standalone servers, an existing Hadoop infrastructure, or in a Virtual Private Cloud (VPC). Also available as a managed cloud service hosted on Amazon Web Services (AWS), delivering the flexibility and speed necessary for any enterprise.

Scalable performance via parallel processing
Leverages modern distributed processing, running experiments in parallel to radically reduce the time it takes to explore, build and tune machine learning models.

Resource monitoring and reporting
Resource Monitor provides a view of platform usage across the organization, including which users and models are consuming computing power, enabling effective resource planning.

Automated model compliance documentation
Can generate documentation describing exactly how a model works for faster compliance and quality reviews, ensuring you can get a model into production in a fraction of the time.

Editable rating tables
Edit and manipulate the model coefficients according to their unique business rules, allowing for an optimal blend of machine learning and human experience.

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