Text Clustering in Einstein Discovery

Text Clustering in Einstein Discovery
It is common to build and deploy supervised machine learning models that are generally comprised of tabular datasets with numerical, categorical, and temporal (date/time) variables. Often though, there may be additional value to be gained by augmenting the model with insights derived from unstructured data (text). Some common examples of unstructured text in this context ...

Predicting the best up-sell with Einstein Discovery Multiclass models

Predicting the best up-sell with Einstein Discovery Multiclass models
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, ...

The complete guide to Einstein Discovery model deployments

The complete guide to Einstein Discovery model deployments
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 ...