Name: Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning (en Inglés)
SKU: 54083793
Price: 71470.00 CLP
Availability: InStock
Author: Lakshmanan, Valliappa
ISBN: 9781098118952

portada Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning (en Inglés)

Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning (en Inglés)

Formato

Libro Físico

Autor

Valliappa Lakshmanan

Editorial

O'Reilly Media

Año

2022

Idioma

Inglés

N° páginas

459

Encuadernación

Tapa Blanda

Dimensiones

23.3x17.8x2.4 cm

Peso

0.73 kg.

ISBN13

9781098118952

N° edición

0002

Categorías

Diseño y teoría de bases de datos
Computación en la nube
Arquitectura de la información

Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning (en Inglés)

Valliappa Lakshmanan (Autor) · O'Reilly Media · Tapa Blanda

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Reseña del libro "Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning (en Inglés)"

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