Chapter 1

1. To leverage the value of intelligent CRM data, automate routine processes, and better connect with your customers on a personal basis
2. Pre-built solutions; Lightning Platform; Tableau CRM; declarative platform services; and programmatic platform services
3. Probabilistic in nature, as well as model-based, data-dependent, autonomous, opaque, evolving, and ethically valent

Chapter 2

1. Einstein Lead Scoring
2. AWS
3. North America

Chapter 3

1. Einstein Article Recommendations
2. Message; rule; action; question
3. Once when you turn the feature on and once when you hit 10,000 chat transcripts

Chapter 4

1. To automatically schedule the best time to send an email
2. In Journey Builder
3. Market basket analysis

Chapter 5

1. Pre-built analytics apps
2. Einstein Discovery
3. Intelligent document automation

Chapter 6

1. Screen flows; recommendation records; strategies; and the Einstein Next Best Action lightning component
2. On a single Salesforce object, exclusively
3. Using write-back, with the Einstein Discovery lightning component, or using the API

Chapter 7

1. Einstein Object Detection.
2. Because I am dealing with a specific domain with its own jargon.
3. You can't fine-tune your own model.

Chapter 8

1. Because your requirements in one dimension or another go beyond what's available on the platform.
2. No, often they can offer an excellent functional fit with a short time-to-value.
3. Azure; AWS; Google Cloud Platform.

Chapter 9

1. Functional, structural, and strategic.
2. Ensure that you have a good grasp of the requirements.
3. The size of the investment.

Chapter 10

1. Using built-in features, using declarative features, and using code-based customization
2. Diving deeply into specific features, expanding your general AI knowledge, and improving your overall AI development skills
3. As a process of balancing trade-offs in an optimal manner

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