In his role as Product Manger of Einstein Discovery at Salesforce / Tableau, Darvish works on creating a world-class machine learning platform, enabling data professionals, and collaborating with customers to help drive their success.
Welcome to part two of the BYOM deep dive. In this post, we will take you through the process of deploying the model we generated in part one of this blog. Similar to part one, the overall structure of this walk-through is contained within two primary phases; some of which also have multiple steps: Deploying ...
Einstein Discovery machine learning helps you build powerful predictive models on your data using clicks, not code. With a simple, wizard-driven interface, you have the ability to rapidly create insights and predictive models. Einstein Discovery utilizes a number of industry-standard algorithms to facilitate building models, these include: GLM (linear and logistic regression) GBM (Gradient Boost ...
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 ...
It is often the case with machine learning predictive models that you need to create a model for customer data that spans a variety of “segments” in your business. The business objective (outcome variable) that you wish to apply machine learning may actually be exactly the same across all these segments – even though the segments themselves ...
🇯🇵 Read in Japanese (Updated 2/10/21) The primary goal of this blog post is to provide technical information on the addition of tree-based machine learning (ML) algorithms to Einstein Discovery. This article helps readers understand the capabilities, benefits, and details of ensemble (tree-based) algorithms in Einstein Discovery. Tree-based algorithms go beyond Einstein Discovery’s traditional focus ...
