Shubha Nabar is the director of data science at Salesforce Einstein. In this Q&A, she discusses how her team is working to make Einstein AI better at serving the needs of businesses of all sizes. What is your role within the Salesforce Einstein group? Shubha Nabar: Think of Salesforce as a platform for building business applications, where there's the sales, service and marketing applications. There's also a rich ecosystem of app developers who build custom applications on the platform.

Customer relationship management service provider Salesforce has released as open source the automated model-building engine that the company uses for its Einstein AI-driven platform. The TransmogrifAI library can be used to build highly-automated machine learning workflows that run on Apache Spark, using the relational data that all organizations keep on hand. TransmogrifAI (pronounced trans-mog-ri-phi) addresses one of the major challenges of setting up machine learning (ML) in production settings, that of establishing a workflow for quickly developing and testing models, which can be used to predict future outcomes. "It automates the entire machine learning workflow. You can build a good machine learning model on a given dataset in a couple hours, instead of weeks or months," Shubha Nabar, Salesforce's senior director of data science for Einstein.

Salesforce is open sourcing the machine learning technology behind its Einstein AI platform. Branded TransmogrifAI, the AutoML library is less than 10 lines of Scala code written on top of Apache Spark, and can be used by developers looking to train machine learning models to predict customer behaviour without having to use a large data set for training. "It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time," it states on its website. In a lengthy Medium post last week, Shubha Nabar, senior director of data science on the Salesforce Einstein team wrote: "Three years ago when we set out to build machine learning capabilities into the Salesforce platform, we learned that building enterprise-scale machine learning systems is even harder."