000			01950nam a22002417a 4500
003			OSt
005			20260602162331.0
008			260602b |||||||| |||| 00| 0 eng d
020			_a978-9355426543 _a9355426542
041			_aeng
082			_a006.31 HAV-I
100			_aHaviv, Yaron _981000
245			_amplementing MLOps in the enterprise: _ba production-first approach
260			_aMumbai _bShroff Publishers & Distributors Pvt. Ltd. _c2023
300			_axiv, 361p.
520			_aThis practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
650			_aArtificial Intelligence _981005
650			_aMachine learning _981006
650			_aMachine learning operations _xMLOps _981007
700			_aGift, Noah _981004
942			_cBK
999			_c200022 _d200022