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Transactional Machine Learning with Data Streams and AutoML: Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python / Sebastian Maurice

By:

Maurice, Sebastian

Material type: Text

Text

TextPublication details: Chennai : Apress , 2022Edition: 1st EditionISBN:

9781484285145

Subject(s):

Machine Learning

DDC classification:

6.3 SEB

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Average rating: 0.0 (0 votes)

Holdings ( 3 )

Holdings
Item type	Current library	Collection	Shelving location	Call number	Status	Barcode
Reference	Kalaignar Centenary Library Madurai	ENGLISH-REFERENCE BOOKS	நான்காம் தளம் / Fourth floor	6.3 SEB (Browse shelf(Opens below))	Not for loan	148356
English Books	Kalaignar Centenary Library Madurai	ENGLISH - LENDING BOOKS	மூன்றாம் தளம் / Third floor	6.3 SEB (Browse shelf(Opens below))	Available	148357
English Books	Kalaignar Centenary Library Madurai	ENGLISH - LENDING BOOKS	மூன்றாம் தளம் / Third floor	6.3 SEB (Browse shelf(Opens below))	Available	148358

Browsing Kalaignar Centenary Library Madurai shelves, Shelving location: மூன்றாம் தளம் / Third floor, Collection: ENGLISH - LENDING BOOKS Close shelf browser (Hides shelf browser)

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Previous	6.3 SAL Deep Learning With Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft Ai Platform /	6.3 SAL Deep Learning With Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft Ai Platform /	6.3 SEB Transactional Machine Learning with Data Streams and AutoML: Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python /	6.3 SEB Transactional Machine Learning with Data Streams and AutoML: Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python /	6.3 SIN Machine Learning With Pyspark: With Natural Language Processing and Recommender Systems /	6.3 SIN Machine Learning With Pyspark: With Natural Language Processing and Recommender Systems /	6.3 SIN Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python /	Next

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