Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP.

Through the course of this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.

You'll learn how to:

• Employ best practices in building highly scalable data and ML pipelines on Google Cloud
• Automate and schedule data ingest using Cloud Run
• Create and populate a dashboard in Data Studio
• Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery
• Conduct interactive data exploration with BigQuery
• Create a Bayesian model with Spark on Cloud Dataproc
• Forecast time series and do anomaly detection with BigQuery ML
• Aggregate within time windows with Dataflow
• Train explainable machine learning models with Vertex AI
• Operationalize ML with Vertex AI Pipelines