Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning Edition: 2

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 with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. 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