000			02042nam a22002657a 4500
003			OSt
005			20260817102840.0
008			260714b |||||||| |||| 00| 0 eng d
020			_a9789385889769
040			_cAKCL
082			_a006.31 _bTRE-I
100			_aTreveil, Mark
245			_aIntroducing MLOps: _bHow to Scale Machine Learning in the Enterprise / _cby Mark Treveil and the Dataiku Team
260			_aMumbai: _bShroff Publishers and Distributors Pvt. Ltd. _c2020
300			_ap.169
520			_aMore than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
650			_a Computer Science
650			_aInformation
650			_aComputing and Information
650			_a Artificial Intelligence
650			_aMachine Learning
942			_cBK
942			_2ddc
942			_2ddc _n0
999			_c44971 _d44971