Module 1: Google Cloud & AI Fundamentals

• Understand Google Cloud regions, zones, and core services

• Learn key Google Cloud services for data and AI

• Explore Vertex AI and responsible AI principles

Module 2: Data Fundamentals for AI on Google Cloud

• Store and manage data with Cloud Storage and BigQuery

• Ingest and process data with Dataflow and Pub/Sub

• Clean, transform, and prepare data for machine learning

Module 3: Core Machine Learning Concepts

• Supervised vs. unsupervised learning

• Train/test split, cross-validation, and evaluation metrics

• Overfitting, underfitting, and regularization

Module 4: Building Models with Vertex AI

• Create datasets, training pipelines, and experiments

• Train and register models using Vertex AI

• Use AutoML to quickly build baseline models

Module 5: Computer Vision & NLP with Google Cloud AI

• Build image analysis solutions with Vision API

• Use Natural Language API for text analytics and sentiment analysis

• Design end-to-end scenarios combining multiple AI services

Module 6: Generative AI on Google Cloud

• Understand large language models and prompt design

• Build chatbots and content generation apps with Vertex AI and Gemini models

• Apply safety, governance, and responsible AI practices

Module 7: MLOps & Deployment on Google Cloud

• Deploy models to online and batch prediction endpoints

• Implement CI/CD for ML with Cloud Build and Cloud Deploy

• Monitor, retrain, and manage models in production

Module 8: Exam Preparation & Practice

• Map each module to the Google Professional Machine Learning Engineer exam objectives

• Review key Google Cloud services, patterns, and best practices

• Practice with exam-style questions and case studies

By the end of this program, learners will be ready to design, build, and deploy AI/ML solutions on Google Cloud and confidently sit for the Google Professional Machine Learning Engineer certification exam.