Book Image

Learning Salesforce Einstein

By : Mohit Shrivatsava
Book Image

Learning Salesforce Einstein

By: Mohit Shrivatsava

Overview of this book

Dreamforce 16 brought forth the latest addition to the Salesforce platform: an AI tool named Einstein. Einstein promises to provide users of all Salesforce applications with a powerful platform to help them gain deep insights into the data they work on. This book will introduce you to Einstein and help you integrate it into your respective business applications based on the Salesforce platform. We start off with an introduction to AI, then move on to look at how AI can make your CRM and apps smarter. Next, we discuss various out-of-the-box components added to sales, service, marketing, and community clouds from Salesforce to add Artificial Intelligence capabilities. Further on, we teach you how to use Heroku, PredictionIO, and the Force platform, along with Einstein, to build smarter apps. The core chapters focus on developer content and introduce PredictionIO and Salesforce Einstein Vision Services. We explore Einstein Predictive Vision Services, along with analytics cloud, the Einstein Data Discovery product, and IOT core concepts. Throughout the book, we also focus on how Einstein can be integrated into CRM and various clouds such as sales, services, marketing, and communities. By the end of the book, you will be able to embrace and leverage the power of Einstein, incorporating its functions to gain more knowledge. Salesforce developers will be introduced to the world of AI, while data scientists will gain insights into Salesforce’s various cloud offerings and how they can use Einstein’s capabilities and enhance applications.
Table of Contents (10 chapters)

Building evaluation metrics for the PredictionIO systems

Let's discuss the methods for performance improvement in general for machine learning systems. The following diagram assumes that we are using the classification algorithm and walks us through the process followed in machine learning systems to evaluate a model. A true machine learning system should use these three sets of data for this purpose:

  • Training dataset
  • Testing dataset
  • Validation dataset

Let's discuss the process of tuning the machine learning accuracy step by step:

  1. We will split the dataset into Training Data and Testing Data. The following diagram demonstrates this concept:
  1. Then, we will build a model on the training set. The following diagram shows the process of training and building a model with Training Data and Testing Data:
  1. Perform an evaluation on the training set, and retrain if needed...