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Tag: Experimentation
Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production
Price: $45.09
(as of Dec 24,2024 22:32:46 UTC – Details)
Publisher : Packt Publishing (July 8, 2022)
Language : English
Paperback : 288 pages
ISBN-10 : 1803241330
ISBN-13 : 978-1803241333
Item Weight : 1.12 pounds
Dimensions : 9.25 x 7.52 x 0.61 inches
Deep learning has revolutionized the field of artificial intelligence, enabling remarkable advancements in areas such as image recognition, natural language processing, and speech recognition. However, deploying deep learning models at scale in production environments remains a significant challenge for many organizations.MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, provides a practical solution for bridging the gap between offline experimentation and online production. By enabling data scientists to track experiments, package code and dependencies, and deploy models in a reproducible and scalable manner, MLflow empowers organizations to streamline their machine learning workflows and accelerate the deployment of deep learning models.
In this post, we will explore how MLflow can be used to facilitate practical deep learning at scale. We will discuss key features of MLflow, such as tracking experiments, managing models, and deploying models to production. Additionally, we will provide real-world examples of how organizations have successfully implemented MLflow to improve their deep learning workflows.
By leveraging MLflow, organizations can effectively manage their machine learning pipelines, reduce time-to-market for new models, and ensure reproducibility and scalability in their deep learning deployments. With MLflow, the gap between offline experimentation and online production can be bridged, enabling organizations to realize the full potential of deep learning at scale.
#Practical #Deep #Learning #Scale #MLflow #Bridge #gap #offline #experimentation #online #production
Methods and Applications of Autonomous Experimentation (Chapman & Hall/CRC Computational Science)
Price:$89.95– $71.96
(as of Nov 24,2024 12:23:06 UTC – Details)
Publisher : Chapman and Hall/CRC; 1st edition (December 14, 2023)
Language : English
Hardcover : 444 pages
ISBN-10 : 1032314656
ISBN-13 : 978-1032314655
Item Weight : 13.45 pounds
Dimensions : 7 x 1.19 x 10 inches
In this post, we will delve into the world of autonomous experimentation and its various methods and applications as discussed in the book “Methods and Applications of Autonomous Experimentation” published by Chapman & Hall/CRC Computational Science.Autonomous experimentation refers to the use of intelligent systems and algorithms to design, conduct, and analyze experiments without human intervention. This book provides a comprehensive overview of the latest advancements in this field, covering topics such as automated design of experiments, autonomous data collection, and machine learning for experiment optimization.
Some of the key methods discussed in the book include Bayesian optimization, reinforcement learning, and active learning, which enable autonomous systems to efficiently explore complex experimental spaces and optimize outcomes. These methods are applied in a wide range of fields, including drug discovery, materials science, and industrial process optimization.
The applications of autonomous experimentation are vast and impactful, offering new opportunities for accelerating scientific discovery and innovation. By automating the experimental process, researchers can uncover new insights, identify optimal conditions, and make data-driven decisions faster and more efficiently than ever before.
Overall, “Methods and Applications of Autonomous Experimentation” provides a valuable resource for researchers, engineers, and data scientists interested in harnessing the power of autonomous systems to revolutionize the way experiments are conducted. Whether you are new to the field or a seasoned practitioner, this book offers valuable insights and practical guidance for leveraging autonomous experimentation in your own work.
#Methods #Applications #Autonomous #Experimentation #Chapman #HallCRC #Computational #Science
