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Amplify and Accelerate Modeling Impact
Machine learning is driving a revolution in risk, fraud, and actuarial modeling. With SigOpt, data scientists can optimally tune the parameters of all their models, from fraud prediction to propensity scoring.
- Any Use Case, One Optimizer: SigOpt provides API-enabled optimization for any model being used to address any use case, whether it is related to fraud, risk, lending or other financial use cases
- Infrastructure Agnostic: This process is entirely agnostic to the underlying data, model, infrastructure or client library that is used – it supports single-cloud, multi-cloud, hybrid-infrastructure or on-premise deployments
- Reproducible Experimentation: Capture actionable insights on every hyperparameter configuration for each training cycle to analyze and reproduce any model
Fully Compliant with Regulatory Needs
SigOpt optimizes any predictive or machine learning model without accessing the underlying data or model. This federated API design ensures no proprietary data leaves your premises, allowing your modeling workflow to stay compliant with regulatory requirements and ensuring the privacy, security and safety of your proprietary information.
- Black-Box Optimization: Black-box optimization never touches the underlying data or models when tuning them, thereby keeping your proprietary information secure, safe and private
- Enterprise Scalability: SigOpt’s API-enabled solution is designed to reliably scale with the modeling needs of any organization as they evolve from a single practitioner to multiple teams across a variety of organizations
- Organizational Control: Assign and manage permissions within a single instance of SigOpt to facilitate collaboration and ensure privacy for every experiment across the full data science team
SigOpt reduces the number of model tuning iterations needed by up to 100x, saving thousands of hours of compute time and cost. The faster your experts iterate on their models, the higher performing these models become – and the greater their impact on your business.
- Efficient Utilization of Resources: When combined with leading hardware, SigOpt increases computational efficiency by 90% to ensure that clusters can be put to work training more models in parallel
- Better Performance: SigOpt includes advanced features like multimetric optimization that are designed to efficiently explore competing objectives, such as the tradeoff between the average cost of fraud and the frequency of its detection
- Faster Time to Market: The combination of Bayesian and other global optimization algorithms efficiently explore and exploit any hyperparameter space to tune models 10x faster than alternative methods