Data Science Box - Data Science as a Service
Putting a predictive model into production and optimizing its performance is complex. Usually, a Data Science team run experiments, develops the model, then hands it over to the development team. A framework and close collaboration between them are crucial for bringing the algorithm into production.
But not every company can employ a Data Science team. That’s why GoDataDriven developed the Data Science Box- a virtual data scientist that monitors the performance of predictive models and reports regularly.
It’s Data Science as a service.
Bringing a predictive model into production
Moving to a DataDriven and predictive way of working are challenges for many companies. Data Science Box makes implementing and maintaining a predictive model simple, even for organizations without an in-house Data Science team.
Data Science Box is Data Science as a service. It analyzes and monitors any predictive model and reports regularly, to your organization and the GoDataDriven Data Science team. Based on predefined thresholds, any sub-optimal performance is exposed and optimized.
Why Data Science Box?
- Better results: A tailor-made predictive model performs better than off-the-shelf models.
- Continuous monitoring: The performance of the box is monitored 24/7. When the model has a sub-optimal performance, GoDataDriven proposes improvements.
- Direct availability of data: Because data is directly available for the development team, issues with integrating a predictive model are now in the past.
- Easy integration: Data Science Box can be deployed out of the box and integrates via RestAPI, JDBC, and file based. Data Science Box taps easily into various data sources, like Hadoop jobs, a JDBC connection to a relational database, or by uploading files.
Building up a Data Science team and bringing a predictive model into production are big challenge for many companies when moving to a DataDriven and predictive way of working. Data Science Box is a virtual data scientist that monitors the performance of predictive models and reports about this regularly.