Description

Introduction

Business analysts and product managers play a key role in identifying AI use cases and ensuring they align with business objectives. Google Vertex AI empowers these professionals with tools like AutoML, data labeling, and explainable AI to extract insights and deploy models without needing deep ML expertise. This module focuses on how to use Vertex AI for rapid prototyping, analysis, and collaboration with data science teams.

Prerequisites

• Familiarity with basic analytics and business KPIs

• Understanding of data formats and spreadsheet-level analysis

• Interest in applying AI to solve business problems

• No prior coding or ML experience required

Table of Contents

1. Introduction to Vertex AI for Business Use
   1.1 Overview of Vertex AI Capabilities
   1.2 Role of Business Analysts and PMs in ML Projects
   1.3 Identifying High-Impact AI Use Cases
   1.4 Collaboration Between Product, Data, and ML Teams

2. Exploring AutoML for No-Code Modeling
   2.1 AutoML Tables for Tabular Data Analysis
   2.2 AutoML Vision for Image Classification Use Cases
   2.3 AutoML Natural Language for Text Classification
   2.4 Creating Models Using Vertex AI UI

3. Preparing Data for Machine Learning
   3.1 Importing Data from BigQuery and CSV Files
   3.2 Data Labeling and Annotation Tools
   3.3 Cleaning and Splitting Data for AutoML
   3.4 Previewing and Validating Dataset Quality

4. Training and Evaluating Models
   4.1 Launching AutoML Model Training from the Console
   4.2 Understanding Model Performance Metrics (Precision, Recall, AUC)
   4.3 Comparing Model Versions and Exporting Results
   4.4 Interpreting Predictions with Explainable AI (XAI)

5. Making Predictions with Trained Models
   5.1 Using Online and Batch Prediction Options
   5.2 Uploading New Data and Getting Insights
   5.3 Integrating Vertex AI Predictions with Business Dashboards
   5.4 Use Cases: Churn Prediction, Lead Scoring, Demand Forecasting

6. Managing AI Projects and Experiments
   6.1 Organizing Vertex AI Projects and Models
   6.2 Tracking Model Experiments and Results
   6.3 Collaborating with Data Scientists and Engineers
   6.4 Version Control and Change Management for Models

7. Ensuring Responsible and Explainable AI
   7.1 Fairness and Bias Detection in AutoML Models
   7.2 Visualizing Feature Importance and Decision Rationale
   7.3 Building Trust in AI with Stakeholders
   7.4 Business Implications of AI Ethics

8. Case Studies and Real-World Applications
   8.1 Marketing: Personalized Campaigns and Segmentation
   8.2 Finance: Credit Risk Scoring and Fraud Detection
   8.3 Retail: Inventory Optimization and Recommendation Engines
   8.4 HR: Attrition Prediction and Resume Screening

9. Best Practices and Next Steps
   9.1 Choosing the Right Use Cases for AutoML
   9.2 Avoiding Common Pitfalls in Business AI Projects
   9.3 Scaling from POCs to Production
   9.4 Working Towards Data-Driven Product Innovation

Vertex AI bridges the gap between technical teams and business stakeholders.
With AutoML, explainability, and a no-code interface, business analysts and product managers can drive AI adoption, make data-informed decisions, and shape impactful products.