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Introducing MLOps
More 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 cyc...
Nonparametric Statistics
Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018. It covers theory, methodology, applications and computational aspects, addressing topics such as nonparametric curve estimation, regression smoothing, models for time series and more generally dependent data, varying coefficient models, symmetry testing, robust estimation, and rank-based methods for factorial design. It also discusses nonparametric and permutation solutions for several different types of data, includ...
Introducing MLOps
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do. This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach--Build, Manage, Deploy and Integrate, and Monitor--for creating ML-infused applications within your organiz...
La Convergence Des Modularités Structurelles Et Fonctionnelles
L'objet de cette th se de doctorat est la convergence structure-fonction dans les syst mes complexes et ses applications aux syst mes vivants et aux syst mes technico- conomiques. Apr s avoir d fini la modularit et identifi les difficult s associ es sa d finition, cette th se formalise le concept de convergence structure-fonction dans les syst mes volutifs et fonctionnels et montre son int r t pour l' volutivit et la robustesse de ces syst mes. Ensuite, elle applique ce concept des probl matiques r elles de syst mes volutifs et fonctionnels en biologie et en conomie afin d'illustrer son utilit . Enfin, elle synth tise l'ensemble de ces r sultats en postulant que la modularit structurelle des syst mes volutifs et fonctionnels tend se superposer leur modularit fonctionnelle afin de leur apporter robustesse et volutivit .
MLOps – Kernkonzepte im Überblick
Erfolgreiche ML-Pipelines entwickeln und mit MLOps organisatorische Herausforderungen meistern Stellt DevOps-Konzepte vor, die die speziellen Anforderungen von ML-Anwendungen berücksichtigen Umfasst die Verwaltung, Bereitstellung, Skalierung und Überwachung von ML-Modellen im Unternehmensumfeld Für Data Scientists und Data Engineers, die nach besseren Strategien für den produktiven Einsatz ihrer ML-Modelle suchen Viele Machine-Learning-Modelle, die in Unternehmen entwickelt werden, schaffen es aufgrund von organisatorischen und technischen Hürden nicht in den produktiven Betrieb. Dieses Buch zeigt Ihnen, wie Sie erprobte MLOps-Strategien einsetzen, um eine erfolgreiche DevOps-Umgebung f...
