Architecting AI Solutions on Salesforce: Design powerful and accurate AI-driven state-of-the-art solutions tailor-made for modern business demands

Length: 340 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-11-12
ISBN-10: 1801076014
ISBN-13: 9781801076012
Sales Rank: #221237 (See Top 100 Books)

Use Salesforce’s out-of-the-box and advanced integration-based AI capabilities to architect modern enterprise solutions on sales, service, marketing, and commerce clouds to drive digital innovation for your clients

Key Features

Get up to speed with Salesforce’s AI features and capabilities to meet ever-evolving client needs
Get expert advice on key architectural decisions and trade-offs when designing AI-driven Salesforce solutions
Integrate third-party AI services into applications that modernize your solutions

Book Description

The ever-increasing need for designing state-of-the-art solutions using AI features requires a sound understanding of a vast array of AI capabilities that help you to architect modern solutions. Salesforce Einstein is a set of services that allows seamless implementation of advanced artificial intelligence (AI) features while retaining the ability to cater to custom requirements for the business.

This book will help you understand the business and technical benefits of building AI solutions and components available in Salesforce. As you work through a case study of a fictional company beginning to adopt AI in its Salesforce ecosystem, you’ll learn how to configure and extend the out-of-the-box features on various Salesforce clouds, their pros, cons, and limitations. You’ll also discover how to extend these features using on- and off-platform choices and how to make the best architectural choices when designing custom solutions. Later, you’ll advance to integrating third-party AI services such as the Google Translation API, Microsoft Cognitive Services, and Amazon SageMaker on top of your existing solutions. This Salesforce book concludes by taking you through key architectural decisions and trade-offs that may impact the design choices you make.

By the end of this book, you’ll be able to architect Salesforce AI solutions to meet various customer requirements confidently.

What you will learn

Explore the AI components available in Salesforce and the architectural model for Salesforce Einstein
Extend the out-of-the-box features using Einstein Services on major Salesforce clouds
Use Einstein declarative features to create your custom solutions with the right approach
Architect AI solutions on marketing, commerce, and industry clouds
Use Salesforce Einstein Platform Services APIs to create custom AI solutions
Integrate third-party AI services such as Microsoft Cognitive Services and Amazon SageMaker into Salesforce

Who this book is for

This book is for existing and aspiring technical and functional architects, technical decision-makers working on the Salesforce ecosystem, and those responsible for designing AI solutions in their Salesforce ecosystem. Lead and senior Salesforce developers who want to start their Salesforce architecture journey will also find this book helpful. Working knowledge of the Salesforce platform is necessary to get the most out of this book.

AI Solutions on the Salesforce Einstein Platform
Salesforce AI for Sales
Salesforce AI for Service
Salesforce AI for Marketing and Commerce
Salesforce AI for Industry Clouds
Declarative Customization Options
Building AI Features with Einstein Platform Services
Integrating Third-Party AI Services
A Salesforce AI Decision Guide
Conclusion

Architecting AI Solutions on Salesforce
Contributors
About the author
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Code in Action
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
Section 1: Salesforce and AI
Chapter 1: AI Solutions on the Salesforce Einstein Platform
    Technical requirements
    Why would you build AI solutions on Salesforce?
        The value of intelligent CRM data
        Some examples
    What are the main components of Salesforce AI?
        The Platform Services layer
        Tableau CRM (previously called Einstein Analytics)
        The Lightning Platform
        Einstein products
        Third-party options
    What are the elements of Salesforce Einstein?
        Einstein for sales
        Einstein for Service
        Einstein for Marketing
        Einstein for Commerce
        Einstein for Industry Clouds
        Declarative Platform Services
        Programmatic Platform Services
    What's special about architecting for AI?
        Probabilistic
        Model-based
        Data-dependent
        Autonomous
        Opaque
        Evolving
        Ethically valent
    Meet Pickled Plastics Ltd.
    Summary
    Questions
Section 2: Out-of-the-Box AI Features for Salesforce
Chapter 2: Salesforce AI for Sales
    Technical requirements
    Introducing Sales Cloud Einstein
    Setting up Einstein Lead Scoring and Opportunity Scoring
        The basics of Einstein Lead Scoring
        Lead Scoring use cases
        Configuring Lead Scoring
        Architectural considerations for Lead Scoring
        Lead scoring at Pickled Plastics Ltd.
        Opportunity Scoring
    Learning about Einstein Forecasting
        The basics of Einstein Forecasting 
        Forecasting use cases
        Configuring Einstein Forecasting
        Architectural considerations for Einstein Forecasting
    Diving into Einstein Activity Capture
        Einstein Activity Capture basics
        Activity Capture use cases
        Configuring Activity Capture
        Architectural considerations for Activity Capture
    Examining Einstein Conversation Insights
        Einstein Conversation Insights basics
        Conversation Insights use cases
        Configuring Conversation Insights
        Architectural considerations for Conversation Insights
    Summary
    Questions
Chapter 3: Salesforce AI for Service
    Technical requirements 
    Introducing Service Cloud Einstein
    Deploying Einstein Bots
        Einstein Bots basics
        Bots use cases
        Configuring Bots
        Architecture considerations for Bots
        Einstein Bots at Pickled Plastics Ltd.
        Einstein Article Recommendations basics
        Article Recommendations use cases
        Configuring Article Recommendations
        Architecture considerations for Article Recommendations
    Speeding up chat with Einstein Reply Recommendations
        Einstein Reply Recommendations basics
        Reply Recommendations use cases
        Configuring Reply Recommendations
        Architecture considerations for Reply Recommendations
        Alleviating manual data entry with Einstein Case Classification
        Case Classification basics
        Case Classification use cases
        Configuring Case classification
        Architectural considerations for Case classification
    Summary
    Questions
Chapter 4: Salesforce AI for Marketing and Commerce
    Technical requirements
    Introducing Einstein for marketing and commerce
    Using Marketing Cloud Einstein
        Einstein Engagement Scoring
        Einstein Engagement Frequency
        Einstein Messaging Insights
        Einstein Copy Insights
        Einstein Splits
        Einstein Send Time Optimization
        Einstein Content Selection
        Einstein Content Tagging
        Einstein Recommendations
        Einstein Social Insights
        Einstein Vision for Social Studio
    Implementing Commerce Cloud Einstein
        Einstein Product Recommendations
        Einstein Predictive Sort
        Einstein Search Dictionaries
        Einstein Commerce Insights
    Summary
    Questions
Chapter 5: Salesforce AI for Industry Clouds
    Technical requirements
    Introducing Einstein for Industry Clouds
    Using Health Cloud Einstein
        Analytics for Healthcare
        Analytics for Healthcare – Risk Stratification
        Einstein Discovery for Appointment Management
    Implementing Financial Services Cloud Einstein
        Tableau CRM for Financial Services
        Einstein Referral Scoring
        Intelligent Document Automation and Form Reader
        Einstein Bots for Financial Services Cloud
    Working with Manufacturing Cloud Einstein
        Tableau CRM for Manufacturing
    Optimizing retail compliance with Consumer Goods Cloud Einstein
        Analytics for Consumer Goods
        Einstein Visit and Visit Task Recommendations
        Einstein Object Detection
    Analyzing with Non-profit Cloud Einstein
        Fundraising Analytics and Performance Analytics
    Summary
    Questions
Section 3: Extending and Building AI Features
Chapter 6: Declarative Customization Options
    Technical requirements 
    Introducing Einstein declarative features
    Giving timely advice with Einstein Next Best Action
        Overview of Einstein Next Best Action
    Predicting outcomes with Einstein Prediction Builder
        Overview of Einstein Prediction Builder
    Generating insights with Einstein Discovery stories
        Overview of Einstein Discovery
    Summary
    Questions
Chapter 7: Building AI Features with Einstein Platform Services
    Technical requirements
    Introducing Einstein Platform Services
    Getting started with the Einstein Vision and Language Model Builder
    Classifying images with Einstein Vision
        Overview of Einstein Vision
    Understanding text with Einstein Language
        Overview of Einstein Language
    Summary
    Questions
Chapter 8: Integrating Third-Party AI Services
    Technical requirements
    Introducing the examples
    Predicting with a custom model using AWS SageMaker
        Coding the machine learning model
    Extracting key phrases with Azure Text Analytics
        Coding the example on Salesforce
    Translating text with Google Translate
    Summary
    Questions
Section 4: Making the Right Decision
Chapter 9: A Salesforce AI Decision Guide
    Using the decision guide
    Choosing the right feature based on functional factors
        Functional fit
        Support for diverse technical use cases
        Support for declarative customization
        Support for code-based customization
        Model configurability
    Choosing the right feature based on structural factors
        Model explainability
        Data volumes supported
        Data requirements
        Model monitoring
        Model compliance
    Choosing the right feature based on strategic factors
        Size of investment
        Model agility
        Skills needed
        Time-to-value
    Applying the framework in practice
    Summary
    Questions
Chapter 10: Conclusion
    Using the power of built-in features
    Extending with declarative features
    Knowing when to go beyond declarative features
    Choosing where to go from here 
        Salesforce AI features
        Custom AI feature development
        General AI background
    Summary
    Questions
Assessments
    Chapter 1
    Chapter 2
    Chapter 3
    Chapter 4
    Chapter 5
    Chapter 6
    Chapter 7
    Chapter 8
    Chapter 9
    Chapter 10
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