In late August 2018, Salesforce announced it had open-sourced its machine learning tool, TransmogrifAI on GitHub. TransmogrifAI is the core software behind Einstein, the company’s in-house AI technology. To many, this signals Salesforce’s intention of making AI a focal point of its platforms.
“I’m excited to learn more about the power of AI at Dreamforce 2018! Team CSS Tec will be attending in full force and AI is an area high on our list to check out.” Says Chuck George, Salesforce Practice Lead/IT Recruiting Manager, CSS Tec, a Contemporary Staffing Solutions Company.
What is TransmogrifAI?
According to Salesforce, TransmogrifAI 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.”
TransmogrifAI is an AutoML library written in Scala that runs primarily on Spark. Developed to accelerate ML developer productivity through automation, its main purpose is to help build ML systems on an enterprise level. It allows for task execution with just a few lines of code, allowing for simpler automation of data cleansing, feature engineering and more.
Why Open Source?
Making TransmogrifAI available through open source will amplify Salesforce’s ML developments. In a blog post announcing the open source move, Shubha Nabar, Senior Director of Data Science at Salesforce Einstein 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 system is even harder…Even if we could build global models, it makes absolutely no sense to do so because every customer’s data is unique, with different schemas, different shapes, and different biases introduced by different business processes. In order to make machine learning truly work for our customers, we have to build and deploy thousands of personalized machine learning models trained on each individual customer’s data for every single use case…
…But we needed a solution that could rapidly produce data-efficient models for heterogeneous structured data at massive scale…Machine learning has the potential to transform how businesses operate, and we believe that barriers to adoption can only be lowered through an open exchange of ideas and code. By working in the open we can bring together diverse perspectives to continue to push the technology forward and make it accessible to everyone.”
Previously, other companies like Google and Oracle have had success with open-sourcing their ML tools. Salesforce’s move was met with much fanfare from the development and AI communities who see endless possibilities for customizing the technology for their unique organizational needs.
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