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The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training, and deployment. The repository is organized into multiple directories that reflect real-world pipelines, such as ingesting data, running SQL-based analytics, streaming data processing, using Spark and Dataproc, applying BigQuery ML, and deploying models with Vertex AI. It emphasizes practical, production-oriented workflows rather than isolated examples, showing how different Google Cloud services interact to form cohesive pipelines.
Features
- End-to-end data science workflows from ingestion to deployment
- Examples for BigQuery, Dataproc, Spark, and Vertex AI
- Streaming and real-time data processing pipelines
- Integration of SQL analytics and machine learning models
- Practical MLOps patterns including automation and monitoring
- Chapter-structured code aligned with a full learning curriculum
Project Samples
