Many software as a service (SaaS) providers across various industries are adding machine learning (ML) and artificial intelligence (AI) capabilities to their SaaS offerings to address use cases like personalized product recommendation, fraud detection, and accurate demand protection. Some SaaS providers want to build such ML and AI capabilities themselves and deploy them in a multi-tenant environment. However, others who have more advanced customers want to allow their customers to build ML models themselves and use them to augment the SaaS with additional capabilities or override the default implementation of certain functionality.
In this post, we discuss how to enhance your SaaS offering with a data science workbench powered by Amazon SageMaker Studio.
Let’s say an independent software vendor (ISV) named XYZ has a leading CRM SaaS offering that is used by millions of customers to analyze customer purchase behavior. A marketer from the company FOO (an XYZ customer) wants

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