| 000 | 02384nam a2200373 i 4500 | ||
|---|---|---|---|
| 001 | 966394369 | ||
| 003 | IIITD | ||
| 005 | 20180418115310.0 | ||
| 008 | 161216s2017 caua 001 0 eng d | ||
| 020 | _a9789352136766 | ||
| 035 |
_a(OCoLC)966394369 _z(OCoLC)966609024 |
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| 040 |
_aYDX _beng _cYDX _dBDX _dBTCTA _dGK8 _dSFR _dCLE _dNYP |
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| 049 | _aNYPP | ||
| 050 | 4 |
_aQA76.54 _b.L35 2018 |
|
| 082 | 0 | 4 |
_a004.33 _223 _bLAK-D |
| 091 | _a004.33 | ||
| 100 | 1 | _aLakshmanan, Valliappa | |
| 245 | 1 | 0 |
_aData science on the google cloud platform : _bimplementing end-to-end real-time data pipelines: from ingest to machine learning _cValliappa Lakshmanan. |
| 260 |
_aNew Delhi : _bO'Reilly, _c©2018. |
||
| 300 |
_axiv, 391 p. : _bill. ; _c24 cm. |
||
| 500 | _aIncludes index. | ||
| 520 | _aLearn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, 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: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines. | ||
| 610 | 2 | 0 | _aGoogle (Firm) |
| 630 | 0 | 0 | _aGoogle Apps. |
| 650 | 0 | _aReal-time data processing. | |
| 650 | 0 | _aCloud computing. | |
| 650 | 0 | _aComputing platforms. | |
| 901 | _aMARS | ||
| 901 |
_ansn _bCATBL |
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| 908 | 4 | _aQA76.54 | |
| 942 |
_2ddc _cBK |
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| 945 | _a.b21374563x | ||
| 946 | _am | ||
| 999 |
_c24329 _d24329 |
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