Developing Data and AI Applications for Retail
Transform retail with data analytics and AI. Build applications that enhance customer experiences, optimize inventory, and drive informed decisions.
AI Usage in Retail
Introduction
Overview
Examples of AI in Retail
Examples of AI in Retail Cont.
Project Overview
Identifying a Regression Scenario
Regression examples
Navigate to Azure ML
Create new Pipeline
Use Sample Data
Select Columns
Clean Missing Data
Normalize Data
Split Data
Train Model
Score Model
Evaluate Model
Successful Pipeline run
Review Results
Analyze Statistical Metrics
More statistical metrics
Try different Model
Create Inference Pipeline
Inference Pipeline Modifications
Submit and Run Inference Pipeline
Deploy Model
Web Service Settings
Test on New Data
Cleanup resources
Summary
Introduction to NLP in Retail
Overview
Language Service / Studio
Configure Settings for Language Studio
Examples of NLP in Retail
Retail Demonstration Intro
Language Service Creation
Language Studio Navigation
Azure's Pre-trained Sentiment Analysis Model
Types of Computer Vision
Microsoft Azure
Optical Character Recognition
OCR and Text Analysis
OCR and Translation
AI in Retail Scenario
Microsoft Azure Resource Creation
OCR in Retail
Introduction
Sephora Virtual Artist
IKEA
Rufus
Walmart
Nike
Congratulations on completing this course!
Build real-world AI applications to solve key challenges in the retail industry
Gain hands-on experience with Azure AI, NLP, and computer vision tools
Use data to enhance customer experience, personalize marketing, and optimize
Self-paced, online learning with weekly live support sessions
Work with real retail data to extract insights and drive strategic decisions
Understand the ethical use of customer data in AI applications