MLflow from Databricks is supported for programming language R

Big Data company Databricks has supported its MLflow machine learning toolkit for the R programming language. New functions have also been launched to increase its usefulness, writes Silicon Angle, among others.

MLflow was unveiled in June. The toolkit should help to standardise the process of developing machine learning applications and to move them to production. According to the company, the process of training machine learning algorithms is very inconsistent and few tools are available to reproduce results, track experiments and manage models.

With MLflow, companies need to be able to better pack, run, test and deploy their machine learning code. Developers get full control to manage the machine learning training lifecycle by making it standard on existing ML toolkits and frameworks with different deployment methods.

RStudio

Databricks has now signed a partnership with RStudio for the new update. RStudio provides an open source and integrated development environment for R to help integrate the programming language. MLflow is now available to the large community of data scientists who use RStudio and R to create new applications.

“The integration of R into MLflow will significantly increase the scope of the project, as a wider community can use and contribute to MLflow,” said RStudio CEO JJ Allaire.

Integrations

MLflow also receives support for programming languages such as Python, Java and Scale. There is also a REST server interface that allows R to be used with other languages. In addition, there have been integrations with popular machine learning libraries and frameworks such as SciKit-Learn, TensorFlow, Keras, PyTorch, H2O and Apache Spark MLlib.

Finally, support for cross-cloud in the MLflow toolkit has become available. This means that models created with MLflow can be deployed in cloud services such as the Azure ML platform, SageMaker from AWS and the Unified Data Analytics platform from Databricks itself.