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Architecting AI Solutions on Salesforce

Architecting AI Solutions on Salesforce

By : Lars Malmqvist
4.8 (13)
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Architecting AI Solutions on Salesforce

Architecting AI Solutions on Salesforce

4.8 (13)
By: Lars Malmqvist

Overview of this book

Written for Salesforce architects who want quickly implementable AI solutions for their business challenges, Architecting AI Solutions on Salesforce is a shortcut to understanding Salesforce Einstein’s full capabilities – and using them. To illustrate the full technical benefits of Salesforce’s own AI solutions and components, this book will take you through a case study of a fictional company beginning to adopt AI in its Salesforce ecosystem. As you progress, 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 isn’t a beginners’ Salesforce book, but a comprehensive overview with practical examples that will also take 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 use Salesforce to design powerful tailor-made solutions for your customers with confidence.
Table of Contents (17 chapters)
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1
Section 1: Salesforce and AI
3
Section 2: Out-of-the-Box AI Features for Salesforce
8
Section 3: Extending and Building AI Features
12
Section 4: Making the Right Decision

Applying the framework in practice

The most important thing to understand about applying this framework for decision-making is that you will never have a perfect fit. There will be different considerations pointing in different directions and you need to assess which factors are the most important.

For instance, when considering a credit scoring use case, you might find that a third-party API provides you with a near-perfect functional fit and a very strong time-to-value. You might even have the skills to implement it. However, on closer examination, the model may not meet your compliance and explainability criteria and thus have to be discarded for legal reasons.

Additionally, you may run up against general architecture and security guidelines within your organization that may override the best fit based on this framework. For instance, you may have strong architecture governance against heavy customization that prohibits a heavily code-based approach, or you may be unable to...

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