Google Cloud Big Data and ML Fundamentals - Course Notes

My comprehensive notes from the Google Cloud Big Data and Machine Learning Fundamentals course covering essential GCP data and ML services.

Course Links:

• Coursera
• Google Cloud Skills Boost

📂 Contents

• 01 - Big Data and ML On Google Cloud - GCP architecture overview, compute and storage services
• 02 - Big Data with BigQuery - Data warehousing, BigQuery ML, and analytics workflows
• 03 - Data Engineering for Streaming Data - Pub/Sub, Dataflow, Apache Beam, and Looker
• 04 - Machine Learning Options on Google Cloud - Pre-built APIs, AutoML, custom training, and Vertex AI
• 05 - Machine Learning Workflow with Vertex AI - Complete ML pipeline from data prep to deployment

🛠️ Key Services Covered

Data & Analytics

• BigQuery - Serverless data warehouse with ML capabilities
• Cloud Storage - Object storage with multiple tiers
• Pub/Sub - Real-time messaging service
• Dataflow - Stream and batch processing with Apache Beam

Machine Learning

• Vertex AI - Unified ML platform
• AutoML - No-code ML model training
• Pre-built APIs - Speech, Vision, Language, Translation
• BigQuery ML - SQL-based machine learning

Visualization & Monitoring

• Looker - Business intelligence platform
• Data Studio - Data visualization tool

🎯 Topics Covered

• Big data architecture patterns on GCP
• Batch vs streaming data processing
• ML model selection and training approaches
• Feature engineering and model evaluation
• MLOps and production deployment strategies
• Data pipeline design and implementation

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These are personal study notes for reference and review purposes.