I'm a Product Manager at Salesforce on the Einstein Discovery team. I have always worked on Data Science and Artificial Intelligence, and I’m very passionate about Einstein Discovery Stories and how embedded augmented intelligence drives better business outcomes!
Data cardinality is an important concept when it comes to Einstein Discovery, but what does it mean exactly? And what considerations should you have around cardinality? Check out this short video to get the answers. Note: Find this video as well as other tips and tricks videos on Einstein Discovery here. Note: The video illustrates ...
Exciting news: with the Spring 22 release, Einstein Discovery supports multiclass classification predictions (Generally Available). This allows you to solve even more predictive use cases for your business with Einstein Discovery. With these Multiclass models, you can predict probable outcomes among up to 10 categories. For example, a manufacturer can predict, based on customer attributes, ...
Have you wondered how much data you need to create a good story in Einstein Discovery? Well, you are not the first one with that questions. Check out this short video to understand what it takes to make a good story. Note: Find this video as well as other tips and tricks videos on Einstein ...
A key pillar in the CRM landscape is pipeline management, where sales representatives track sales opportunities that progress through various stages until closure. This process results in either winning the specific sales opportunity or closing the opportunity without winning the business. The latter may be because the opportunity was lost to a competitor, because the ...
Einstein Discovery drives business value for companies by eliminating friction in using machine learning, and maximizing its time-to-value. It is designed to facilitate every step of the journey towards operationalizing Machine Learning in the workspace, in a safe, ethical and most of all practical and easy way. This applies training the model and interpreting the Story, but it ...
🇯🇵 Read in Japanese Einstein Discovery allows the business scientist to explore patterns, trends and correlations in business data using Stories. The Story answers various questions, depending on the data it was trained on. Examples include Opportunity win-rate analysis, proprensity-to-buy (PTB) and Case average handling-time (AHT) or satisfaction (CSAT) in customer service. One particularly useful component that results automatically from ...
The accuracy of Machine Learning models gives rise to one of the most confusing discussions in the world of Machine Learning. There are multiple reasons for that. First, many different performance metrics are used, which makes fair comparisons and transparent discussions hard. Secondly, expectations are often unrealistically high, caused by extremely overblown media coverage of ...
You created a story in Einstein Discovery. You measured its model’s accuracy. Then you made some necessary improvements. The model you deployed is now bringing predictions and recommendations right to your users’ fingertips. You’re pretty satisfied with what you achieved. Cool, you’re done. Your model is out in the wild now. Onto the next adventure, ...
