Winter 2020 Release

Every Machine Learning project starts with data and data can come from many sources. This is especially true for complex enterprise computing environments. In responding to the need to import data directly from external databases to streamline Machine Learning workflows, BigML now supports MySQL, SQL Server, and Elasticsearch in addition to PostgreSQL.

Both the BigML Dashboard and the API allow you to establish a connector to your data store by providing relevant connection and authentication information, which are encrypted and stored in BigML for future access. BigML can then connect to your data store and immediately create the Source in BigML's server(s). You have the options to import data from individual tables or to do it selectively by specifying the data spanning multiple tables via custom queries. In an organization setting, the administrators can easily create connectors to be used by other members of the same organization.

Anything you create on the BigML Dashboard, you can replicate with the BigML API. Now, BigML has added the ability to preview an API request as part of the configuration of unsupervised and supervised models including Fusions on the Dashboard.

This feature essentially shows the users how to create the resource programmatically. It includes the endpoint of the REST API call and the JSON file that specifies the arguments to be configured.

When you use WhizzML scripts, some inputs may be mandatory, some optional. You may also provide default values to inputs. You can specify those in the corresponding JSON metadata files. Now, you can also do this on the BigML Dashboard when inputs are resources like Sources, Datasets and Models. BigML provides checkboxes for users to easily toggle between those inputs, which can be set as mandatory or optional. Similarly, users also have the option to provide default values for those inputs or leave them empty in the BigML Dashboard.