Enhancing Analytics Teams

Knowledge Sharing, Reusable Models, Automation Tools

For Data Scientists

We enable data scientists to deliver business value more efficiently with shared knowledge on problem structure, testing structure, and repositories of models for re-use across multiple use cases. Automation of critical elements of the data science lifecycle are offered, for testing and validation. Our platform becomes a valuable learning tool and enhances each user’s ability to work on numerous business problems. We offer R and Python options for model generation and automation modules.

For Data Scientists

We enable data scientists to deliver business value more efficiently with shared knowledge on problem structure, testing structure, and repositories of models for re-use across multiple use cases. Automation of critical elements of the data science lifecycle are offered, for testing and validation. Our platform becomes a valuable learning tool and enhances each user’s ability to work on numerous business problems. We offer R and Python options for model generation and automation modules.

For Project Managers

We offer the ability to view and manage all analytics projects in one console. Cost per prediction, model performance, alerts, resource assignment, and use case templates enable managers to run their teams efficiently and experience higher throughput. With today’s shortage of highly trained and experienced data scientists, we provide a cost-effective solution to enhance existing teams, and can manage and optimize live models in production, while existing teams focus on new use cases.

For Project Managers

We offer the ability to view and manage all analytics projects in one console. Cost per prediction, model performance, alerts, resource assignment, and use case templates enable managers to run their teams efficiently and experience higher throughput. With today’s shortage of highly trained and experienced data scientists, we provide a cost-effective solution to enhance existing teams, and can manage and optimize live models in production, while existing teams focus on new use cases.