Features

DSML platforms easily integrate many advanced features, including:

• Create data access pipelines, data preparation steps, algorithms and predictive models using a graphical approach, without coding.
• Access with predefined connectors to a variety of formats and data sources, from big data to the cloud
• Integration of open source tools, to exploit the consolidated heritage and adopt the most specialized AI innovations
• Support the end-to-end management of analytical models, including collaboration and sharing, implementation and automatic monitoring of results and performances.
• Enable the use of advanced deep-learning ML frameworks in an easy way

Benefits

• Allowing advanced users of spreadsheets to code and evolve algorithms on enterprise-level tools without the commitment and hidden costs of learning and using a complex language such as Python or R.
• Quickly create internal company skills with limited training for the development of predictive, prescriptive and deterministic solutions with internal resources or through service providers, while maintaining control over intellectual property, including self-documentation and self-explicability
• Facilitate collaboration between data scientists, engineers and traditional developers and the emerging roles of citizen data scientists
• Provide great visibility into the operations of data science teams.
• Support a sharing environment in which data scientists can interact with the business on projects in a simple way
• Quickly produce quality data science solutions specifically tailored to business cases, to differentiate themselves from pre-trained off-the-shelf models