000			01667nam a22002417a 4500
005			20260708171716.0
008			260708s2024 |||||||| |||| 00| 0 eng d
020			_a9781805122500
040			_cPK-LaUMT
082			_a006.31 _bPIN-M
100	1		_aPing, David _913450
245	1	4	_aThe machine learning solutions architect handbook / _cDavid Ping
250			_a2nd ed.
260			_aBirmingham : _bPackt Publishing, _c2024
300			_axxii, 569 p.
500			_aIndex present
520			_aDesign, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook Key Features Solve large-scale ML challenges in the cloud with several open-source and AWS tools and frameworks Apply risk management techniques in the ML life cycle and learn architecture patterns for solutions Understand the challenges and risks of implementing generative AI Book Description David Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications.
546			_aEng
650			_aMachine learning
650			_aComputer architecture
650			_aSystem design
942			_cBK
999			_c141343 _d141343