000			01975nam a22002897i 4500
005			20250820112747.0
008			250723s2023 ii ao||fr|||| 00| 0 eng d
020			_a9789355513267
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082	0	4	_223 _a006.3 _bR.P.C
100			_aRangarajan, Premkumar, _eauthor. _960485
245	1	0	_aCloud native AI and machine learning on AWS : _buse SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services / _cPremkumar Rangarajan, David Bounds.
250			_a1st edition.
264		1	_aNew Delhi : _b$bBPB Publications, _c2023.
300			_a400 pages : _billustrations ; _c24 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references.
505			_a505 0\$a1. Introducing the ML Workflow ‡ 2. Hydrating the Data Lake ‡ 3. Predicting the Future With Features ‡ 4. Orchestrating the Data Continuum ‡ 5. Casting a Deeper Net (Algorithms and Neural Networks) ‡ 6. Iteration Makes Intelligence (Model Training and Tuning) ‡ 7. Let George Take Over (AutoML in Action) ‡ 8. Blue or Green (Model Deployment Strategies) ‡ 9. Wisdom at Scale with Elastic Inference ‡ 10. Adding Intelligence with Sensory Cognition ‡ 11. AI for Industrial Automation ‡ 12. Operationalized Model Assembly (MLOps and Best Practices).
520			_aThis book guides data and cloud professionals to design, deploy, and manage end-to-end AI/ML systems on AWS using SageMaker, Comprehend, Rekognition, Lookout, and AutoML. It covers MLOps automation, neural networks, data lakes, operational best practices, and real-world case studies. :contentReference[oaicite:1]{index=1}
650		0	_aMachine learning _xImplementation. _960427
650		0	_aCloud computing.
700	1		_0Bounds, David,$eauthor.
942			_2ddc _cBK
999			_c11089 _d11089