TransmogrifAI From Salesforce
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Salesforce plans to open-source the technology behind its Einstein machine-learning services
Salesforce is open-sourcing the method it has developed for using machine-learning techniques at scale — without mixing valuable customer data — in hopes other companies struggling with data science problems can benefit from its work.
The company plans to announce Thursday that TransmogrifAI, which is a key part of the Einstein machine-learning services that it believes are the future of its flagship Sales Cloud and related services, will be available for anyone to use in their software-as-a-service applications. Consisting of less than 10 lines of code written on top of the widely used Apache Spark open-source project, it is the result of years of work on training machine-learning models to predict customer behavior without dumping all of that data into a common training ground, said Shubha Nabar, senior director of data science for Salesforce Einstein.
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Salesforce open-sources TransmogrifAI, the machine learning library that powers Einstein
Machine learning models — artificial intelligence (AI) that identifies relationships among hundreds, thousands, or even millions of data points — are rarely easy to architect. Data scientists spend weeks and months not only preprocessing the data on which the models are to be trained, but extracting useful features (i.e., the data types) from that data, narrowing down algorithms, and ultimately building (or attempting to build) a system that performs well not just within the confines of a lab, but in the real world.
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