最新章節

• Summary
• The Get model learning curve
• The Get model metrics
• Measuring the accuracy of Salesforce Einstein Vision
• Evaluation in PredictionIO
• The confusion matrix

• Summary 更新時間：2021-07-02 21:44:39
• The Get model learning curve
• The Get model metrics
• Measuring the accuracy of Salesforce Einstein Vision
• Evaluation in PredictionIO
• The confusion matrix
• The f1 score
• Precision and recall
• Accuracy
• Building the PredictionIO evaluation module
• Cross Validation
• ML tuning and evaluation in PredictionIO
• Building evaluation metrics for the PredictionIO systems
• Measuring the accuracy of Opportunity Insights
• Salesforce report to measure the accuracy of Lead Score
• Which lead field values affect conversion rates the most?
• Measuring the accuracy of the Einstein Lead Scoring engine
• Measuring the accuracy of Sales Cloud Einstein
• Measuring and Testing the Accuracy of Einstein
• Summary
• Using PredictionIO on IoT events
• Request and Response samples for polling the token endpoint
• Request and Response for device initiating authentication flow
• OAuth 2.0 authentication flow for applications on limited input devices
• Asset token explorer app
• Prerequisites for implementing asset token flow in Salesforce
• OAuth 2.0 Asset Token Flow for securing connected devices
• Introducing identity for the Internet of Things
• Writing unit Apex tests for platform events
• Using CometD to subscribe to platform events
• Subscribe to the platform events
• Publish platform events
• Creating platform events
• Introducing platform events
• IoT integration on the Salesforce Force.com platform
• Consuming Kafka messages
• Publish interaction events to Kafka
• Initializing producer in your Node.js application
• Import the Kafka Node.js module
• Encrypting the connection between Kafka and the Heroku web app
• Node.js sample code for producers and consumers
• Supported languages
• Apache Kafka on Heroku
• Kafka API
• Apache Kafka on Heroku
• Orchestrations
• Data Pipes and data transformation
• Input streams and data connections
• IoT Cloud components
• Traffic view
• Orchestration
• State machine
• IoT Cloud key terms
• Einstein and Salesforce IoT Cloud Platform
• Summary
• Creating stories
• Creating datasets from Salesforce objects
• Importing Salesforce data into Einstein Data Discovery and creating stories
• Sign up for a trial organization
• Introduction to Einstein Data Discovery
• Row-level Security for dataset
• XMD 2.0
• Salesforce Analytics Query Language
• Wave Analytics SAQL XMD 2.0 and dataset Row-Level Security
• An update transformation
• The sliceDataset transformation
• The flatten transformation
• The filter transformation
• The edgemart transformation
• The dim2mea transformation
• The delta transformation
• The computeRelative transformation
• The computeExpression transformation
• The augment transformation
• The append transformation
• The sfdcRegister transformation
• The sfdcDigest transformation
• Using transformations to create dataset
• Scheduling dataflow
• Creating interactive dashboards
• Creating a lens from dataset
• Dataflow and data manager
• Creating a dataset
• Creating datasets lenses and dashboards
• Creating and assigning permission sets
• Enabling access and permissions to the Analytics Cloud
• Setting up Wave Analytics Cloud
• Einstein for Analytics Cloud
• Summary
• Creating Lightning Components to recognize image
• Trigger automation for dataset creation and training the model
• Application and tabs
• Data model
• Creating an administration app for creating and training dataset
• Train dataset using Apex
• Monitoring status of training
• Creating dataset using Apex
• Creating and training dataset via Apex
• JWT Bearer token exchange
• Constructing JWT Encoded Body
• Apex code utility to obtain access token
• Securing Private Key
• Remote Site settings for Einstein API
• Set up authorization between Salesforce and Einstein Vision APIs
• Building Applications Using Einstein Vision and Salesforce Force.com Platform
• Summary
• Limitations of the application
• Deployment on Heroku instance
• Testing the application on localhost
• The fileupload.js file
• The update-token.js file
• The Episode7 module
• Middleware using Express
• The results.js file
• The App.js file
• The index.js file
• Scaffolding a React App
• Building React UI for image upload
• Building Node.js application using Einstein Vision on Heroku using React
• Obtaining the access token from Private Key
• Procfile
• Authorization setup
• Set up the Heroku add-on for Einstein Vision Services
• Prediction with image file
• Get status of the training
• Train the dataset
• Get status of the upload
• Creating a dataset from a zip file asynchronously
• Creation of dataset
• Explore Einstein Vision APIs
• Signing up for Einstein Vision account
• Salesforce Einstein Vision
• Summary
• GitHub references
• PredictionIO commands cheat sheet
• The SimilarProductRecommender component
• The ProductViewCapture component
• Custom settings for configuration parameters
• Building similar Recommendation Lightning Component for App Cloud
• Apex controller class
• Component CSS file
• JavaScript Helper
• JavaScript controller
• Component
• Lightning Component framework
• Visualforce
• Introduction to building Lightning Component for App Cloud and Community Cloud
• Setting up IntelliJ IDEA IDE for customizing PredictionIO application
• TrainApp
• ServerApp
• PredictionIO Engine template code explanation
• Setting up the Recommendation engine application on Heroku
• Event Server code explanation
• Setting up the Event Server app on Heroku
• Introduction to Spark MLlib
• Product Recommendation Application using PredicitionIO and Salesforce App Cloud
• Summary
• Deploying the Template Engine
• Deploying an Event Server application
• Heroku Buildpack for PredictionIO
• Deploying PredictionIO on Heroku
• Serving
• Algorithm
• Data
• Spark MLlib
• Predicted response design
• Query data structure
• Engine design
• PredictionIO DASE components and customization of Engine
• Getting started with PredictionIO
• Installing and configuring PredictionIO Event Server
• Prerequisites
• Installation of PredictionIO
• Integration with web/mobile applications
• Architecture and integration with applications
• PredictionIO platform components
• Introduction to PredictionIO
• Building Smarter Apps Using PredictionIO and Heroku
• Summary
• Personalization Builder
• Social Studio Einstein features
• Marketing Cloud Einstein features
• Recommended experts articles and topics
• Creating Salesforce Communities
• Unanswered questions component for Community Builder
• The Company Highlights feature on Chatter
• Community Cloud Einstein features
• Account Insights
• Opportunity Insights
• Lead Insights
• Automated Activity Capture
• Sales Cloud Einstein offerings
• Role of AI in CRM and Cloud Applications
• Summary
• Drawback of this approach
• Setting authentication for calling API from SFDC
• Integration architecture
• Training and prediction
• Prerequisites
• Business scenario
• Practical machine learning with Google Prediction API and Salesforce
• Programming languages used for machine learning
• GPUs
• Data mining
• Pattern recognition
• Cognitive computing
• Natural language processing
• Deep Learning
• Neural networks
• Machine Learning
• Artificial Intelligence key terms
• Introduction to AI
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• Introduction to AI
• Artificial Intelligence key terms
• Machine Learning
• Neural networks
• Deep Learning
• Natural language processing
• Cognitive computing
• Pattern recognition
• Data mining
• GPUs
• Programming languages used for machine learning
• Practical machine learning with Google Prediction API and Salesforce
• Business scenario
• Prerequisites
• Training and prediction
• Integration architecture
• Setting authentication for calling API from SFDC
• Drawback of this approach
• Summary
• Role of AI in CRM and Cloud Applications
• Sales Cloud Einstein offerings
• Automated Activity Capture
• Lead Insights
• Opportunity Insights
• Account Insights
• Community Cloud Einstein features
• The Company Highlights feature on Chatter
• Unanswered questions component for Community Builder
• Creating Salesforce Communities
• Recommended experts articles and topics
• Marketing Cloud Einstein features
• Social Studio Einstein features
• Personalization Builder
• Summary
• Building Smarter Apps Using PredictionIO and Heroku
• Introduction to PredictionIO
• PredictionIO platform components
• Architecture and integration with applications
• Integration with web/mobile applications
• Installation of PredictionIO
• Prerequisites
• Installing and configuring PredictionIO Event Server
• Getting started with PredictionIO
• PredictionIO DASE components and customization of Engine
• Engine design
• Query data structure
• Predicted response design
• Spark MLlib
• Data
• Algorithm
• Serving
• Deploying PredictionIO on Heroku
• Heroku Buildpack for PredictionIO
• Deploying an Event Server application
• Deploying the Template Engine
• Summary
• Product Recommendation Application using PredicitionIO and Salesforce App Cloud
• Introduction to Spark MLlib
• Setting up the Event Server app on Heroku
• Event Server code explanation
• Setting up the Recommendation engine application on Heroku
• PredictionIO Engine template code explanation
• ServerApp
• TrainApp
• Setting up IntelliJ IDEA IDE for customizing PredictionIO application
• Introduction to building Lightning Component for App Cloud and Community Cloud
• Visualforce
• Lightning Component framework
• Component
• JavaScript controller
• JavaScript Helper
• Component CSS file
• Apex controller class
• Building similar Recommendation Lightning Component for App Cloud
• Custom settings for configuration parameters
• The ProductViewCapture component
• The SimilarProductRecommender component
• PredictionIO commands cheat sheet
• GitHub references
• Summary
• Salesforce Einstein Vision
• Signing up for Einstein Vision account
• Explore Einstein Vision APIs
• Creation of dataset
• Creating a dataset from a zip file asynchronously
• Get status of the upload
• Train the dataset
• Get status of the training
• Prediction with image file
• Set up the Heroku add-on for Einstein Vision Services
• Authorization setup
• Procfile
• Obtaining the access token from Private Key
• Building Node.js application using Einstein Vision on Heroku using React
• Building React UI for image upload
• Scaffolding a React App
• The index.js file
• The App.js file
• The results.js file
• Middleware using Express
• The Episode7 module
• The update-token.js file
• The fileupload.js file
• Testing the application on localhost
• Deployment on Heroku instance
• Limitations of the application
• Summary
• Building Applications Using Einstein Vision and Salesforce Force.com Platform
• Set up authorization between Salesforce and Einstein Vision APIs
• Remote Site settings for Einstein API
• Securing Private Key
• Apex code utility to obtain access token
• Constructing JWT Encoded Body
• JWT Bearer token exchange
• Creating and training dataset via Apex
• Creating dataset using Apex
• Monitoring status of training
• Train dataset using Apex
• Creating an administration app for creating and training dataset
• Data model
• Application and tabs
• Trigger automation for dataset creation and training the model
• Creating Lightning Components to recognize image
• Summary
• Einstein for Analytics Cloud
• Setting up Wave Analytics Cloud
• Enabling access and permissions to the Analytics Cloud
• Creating and assigning permission sets
• Creating datasets lenses and dashboards
• Creating a dataset
• Dataflow and data manager
• Creating a lens from dataset
• Creating interactive dashboards
• Scheduling dataflow
• Using transformations to create dataset
• The sfdcDigest transformation
• The sfdcRegister transformation
• The append transformation
• The augment transformation
• The computeExpression transformation
• The computeRelative transformation
• The delta transformation
• The dim2mea transformation
• The edgemart transformation
• The filter transformation
• The flatten transformation
• The sliceDataset transformation
• An update transformation
• Wave Analytics SAQL XMD 2.0 and dataset Row-Level Security
• Salesforce Analytics Query Language
• XMD 2.0
• Row-level Security for dataset
• Introduction to Einstein Data Discovery
• Sign up for a trial organization
• Importing Salesforce data into Einstein Data Discovery and creating stories
• Creating datasets from Salesforce objects
• Creating stories
• Summary
• Einstein and Salesforce IoT Cloud Platform
• IoT Cloud key terms
• State machine
• Orchestration
• Traffic view
• IoT Cloud components
• Input streams and data connections
• Data Pipes and data transformation
• Orchestrations
• Apache Kafka on Heroku
• Kafka API
• Apache Kafka on Heroku
• Supported languages
• Node.js sample code for producers and consumers
• Encrypting the connection between Kafka and the Heroku web app
• Import the Kafka Node.js module
• Initializing producer in your Node.js application
• Publish interaction events to Kafka
• Consuming Kafka messages
• IoT integration on the Salesforce Force.com platform
• Introducing platform events
• Creating platform events
• Publish platform events
• Subscribe to the platform events
• Using CometD to subscribe to platform events
• Writing unit Apex tests for platform events
• Introducing identity for the Internet of Things
• OAuth 2.0 Asset Token Flow for securing connected devices
• Prerequisites for implementing asset token flow in Salesforce
• Asset token explorer app
• OAuth 2.0 authentication flow for applications on limited input devices
• Request and Response for device initiating authentication flow
• Request and Response samples for polling the token endpoint
• Using PredictionIO on IoT events
• Summary
• Measuring and Testing the Accuracy of Einstein
• Measuring the accuracy of Sales Cloud Einstein
• Measuring the accuracy of the Einstein Lead Scoring engine
• Which lead field values affect conversion rates the most?
• Salesforce report to measure the accuracy of Lead Score
• Measuring the accuracy of Opportunity Insights
• Building evaluation metrics for the PredictionIO systems
• ML tuning and evaluation in PredictionIO
• Cross Validation
• Building the PredictionIO evaluation module
• Accuracy
• Precision and recall
• The f1 score
• The confusion matrix
• Evaluation in PredictionIO
• Measuring the accuracy of Salesforce Einstein Vision
• The Get model metrics
• The Get model learning curve
• Summary 更新時間：2021-07-02 21:44:39