Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning 2nd
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
Valliappa (Lak) Lakshmanan is the director of analytics and AI solutions at Google Cloud, where he leads a team building cross-industry solutions to business problems. His mission is to democratize machine learning so that it can be done by anyone anywhere. Lak is the author or coauthor of Practical Machine Learning for Computer Vision, Machine Learning Design Patterns, Data Governance The Definitive Guide, Google BigQuery The Definitive Guide, and Data Science on the Google Cloud Platform.