Data science on the Google cloud platform : (Record no. 1010)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 01902nam a22002297a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230312220524.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230312b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | 0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 004.33 LAK |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Edition number | 0 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lakshmanan, Valliappa |
| 245 ## - TITLE STATEMENT | |
| Title | Data science on the Google cloud platform : |
| Remainder of title | implementing end-to-end real-time data pipelines : from ingest to machine learning |
| Statement of responsibility, etc. | Valliappa Lakshmanan |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Sebastopol, CA |
| Name of publisher, distributor, etc. | O'Reilly |
| Date of publication, distribution, etc. | 2016 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxiv, 492 pages : |
| Other physical details | illustrations (some color) |
| Dimensions | 23 cm |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Cloud computing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computing platforms |
| 843 ## - REPRODUCTION NOTE | |
| Type of reproduction | Photocopy |
| 887 ## - NON-MARC INFORMATION FIELD | |
| Source of data | CamTech Library |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Suppress in OPAC | No |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Copy number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | CamTech Library | CamTech Library | FaE's Corner, Faculty of Engineering | 03/12/2023 | 1 | 004.33 LAK | CamTech 001126 | 08/08/2023 | 03/12/2023 | C.1 | 03/12/2023 | Books |