Data science on the Google Cloud Platform: implementing end-to-end real-time data pipelines : from ingest to machine learning
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. Thro...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
O'Reilly
2022
|
| Ausgabe: | Second edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781098118945/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Previous edition: Sebastopol: O'Reilly, 2018. - Description based on print version record |
| Umfang: | 1 Online-Ressource (1 volume) |
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| spelling | Lakshmanan, Valliappa VerfasserIn aut Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning Valliappa Lakshmanan Second edition. Cambridge O'Reilly 2022 1 Online-Ressource (1 volume) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous edition: Sebastopol: O'Reilly, 2018. - Description based on print version record 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. Google (Firm) Real-time data processing Cloud computing Computing platforms Temps réel (Informatique) Infonuagique Plateformes (Informatique) 9781098118952 Erscheint auch als Druck-Ausgabe 9781098118952 |
| spellingShingle | Lakshmanan, Valliappa Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning Google (Firm) Real-time data processing Cloud computing Computing platforms Temps réel (Informatique) Infonuagique Plateformes (Informatique) |
| title | Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning |
| title_auth | Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning |
| title_exact_search | Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning |
| title_full | Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning Valliappa Lakshmanan |
| title_fullStr | Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning Valliappa Lakshmanan |
| title_full_unstemmed | Data science on the Google Cloud Platform implementing end-to-end real-time data pipelines : from ingest to machine learning Valliappa Lakshmanan |
| title_short | Data science on the Google Cloud Platform |
| title_sort | data science on the google cloud platform implementing end to end real time data pipelines from ingest to machine learning |
| title_sub | implementing end-to-end real-time data pipelines : from ingest to machine learning |
| topic | Google (Firm) Real-time data processing Cloud computing Computing platforms Temps réel (Informatique) Infonuagique Plateformes (Informatique) |
| topic_facet | Google (Firm) Real-time data processing Cloud computing Computing platforms Temps réel (Informatique) Infonuagique Plateformes (Informatique) |
| work_keys_str_mv | AT lakshmananvalliappa datascienceonthegooglecloudplatformimplementingendtoendrealtimedatapipelinesfromingesttomachinelearning |