000			03527cam a22005417i 4500
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006			m d
007			cr cnu---unuuu
008			220518s2022 enka o 000 0 eng d
040			_aORMDA _beng _erda _epn _cORMDA _dUKMGB _dN$T _dOCLCF _dYDX
015			_aGBC274210 _2bnb
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019			_a1329305939
020			_a9781803246796 _qelectronic book
020			_a1803246790 _qelectronic book
020			_z9781803232416
035			_a3274268 _b(N$T)
035			_a(OCoLC)1317831602 _z(OCoLC)1329305939
037			_a9781803232416 _bO'Reilly Media
050		4	_aQ325.5 _bK67 2022
082	0	4	_a006.3/1 _223/eng/20220518
049			_aMAIN
100	1		_aK�orner, Christoph, _eauthor. _919752
245	1	0	_aMastering Azure machine learning : _bexecute large-scale end-to-end machine learning with Azure / _cChristoph K�orner, Marcel Alsdorf.
246	3	0	_aExecute large-scale end-to-end machine learning with Azure
250			_aSecond edition.
264		1	_aBirmingham, UK : _bPackt Publishing Ltd., _c2022.
300			_a1 online resource (624 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aSupercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services. Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline.
590			_aWorldCat record variable field(s) change: 050
650		0	_aMachine learning. _92890
650		0	_aCloud computing. _95598
650		0	_aMicrosoft Azure (Computing platform) _919753
650		7	_aCloud computing. _2fast _0(OCoLC)fst01745899 _95598
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795 _92890
650		7	_aMicrosoft Azure (Computing platform) _2fast _0(OCoLC)fst01940548 _919753
655		4	_aElectronic books. _93907
700	1		_aAlsdorf, Marcel, _eauthor. _919754
776	0	8	_iPrint version : _z9781803232416
856	4	0	_3EBSCOhost _uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3274268
938			_aEBSCOhost _bEBSC _n3274268
994			_a92 _bN$T
999			_c8284 _d8284