Ingestion pipelines ensure your data flow is smooth, continuous, and governed. Code libraries, coding standards, version control, and pipelines that automate testing, ensure that the data team can visualize and create models in a repeatable way. Options for tight user-access control are available at multiple levels, integrated with your enterprise directory. Encryption is optional, and access logging is available as with any data factory. Multiple built-in security features ensure compliance with GDPR requirements.

With our platform — consisting of pre-installed data science libraries, multiple coding skeletons, and visualization features — data scientists can perform analytics and data science in the most efficient and effective way. They are not bothered with technical hassle. Instead, they drive the pre-processing, and create and train data-science models in an agile way of working. By doing this, they can smartify data fast, achieving results in as little as a few days — or fail fast and move to the next use case. And, if a use case is proven to drive business value, it can quickly be productionalized to run. 

We also assist your data scientists by providing pipelines to handle the workflow, including automated testing of the created models and their versions.

Automation pipelines in the data factory make it very easy to spin up a development environment to test varied machine learning models. Built-in code libraries and tooling will speed up development. And as soon as a model has proven its value, the deployment pipeline enables you to rapidly deploy to production. Version control, automated unit tests, and integration tests ensure that this occurs in a controlled and repeatable manner.