December 28, 2019

347 words 2 mins read

The journey to Einstein: Building a multitenancy AI platform that powers hundreds of thousands of businesses

The journey to Einstein: Building a multitenancy AI platform that powers hundreds of thousands of businesses

Salesforce recently released Einstein, which brings AI into its core platform to power every business. The secret behind Einstein is an underlying platform that accelerates AI development at scale for both internal and external data scientists. Simon Chan shares his experience building this unified platform for a multitenancy, multibusiness cloud enterprise.
Talk Title The journey to Einstein: Building a multitenancy AI platform that powers hundreds of thousands of businesses
Speakers Simon Chan (Salesforce)
Conference Strata Data Conference
Conf Tag Make Data Work
Location New York, New York
Date September 26-28, 2017
URL Talk Page
Slides Talk Slides
Video

Artificial intelligence is a game changer that’s transforming the very foundation of how businesses interact with customers. Salesforce is bringing AI to every business through its comprehensive set of business lines, such as sales, service, marketing, commerce, community, analytics, and the IoT. Salesforce recently released Einstein, which brings AI into its core platform to power every business. In addition to enabling prebuilt predictive applications, Einstein empowers customers to quickly build AI-powered apps that include Einstein-powered fields in any object, page layout, or workflow, making every business process smarter. For data scientists and developers, Einstein offers predictive vision and sentiment services that enable developers to train deep learning models to recognize and classify images and sentiment in text. And PredictionIO in Heroku Private Spaces empowers developers to build custom machine learning models and put them into their apps. The secret behind Einstein is an underlying platform that accelerates AI development at scale for both internal and external data scientists. Simon Chan shares his experience building this unified AI platform to power advanced machine learning, deep learning, natural language processing, and smart data discovery for multiple enterprise product lines. Simon discusses the challenges involved in enabling ML models to be automatically customized for every single customer in a multitenancy cloud business and handling various technologies employed by different teams, such as SparkML and TensorFlow, before leading a deep dive into the cross-cloud data platform architecture. Along the way, he shares best practices for building an AI platform for large-scale production deployment.

prediction intelligence intel spark tensorflow ml large-scale ai analytics deep learning machine learning cloud artificial intelligence
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