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02766aam a2200337Ii 4500 001 ABAF56EEA55F11EAA027EF1497128E48 003 SILO 005 20200603010033 008 190630t20202020caua 001 0 eng d 020 $a 9781492045113 020 $a 149204511X 035 $a (OCoLC)1106176581 040 $a YDX $b eng $e rda $c YDX $d BDX $d GK8 $d DAD $d OCLCF $d UKMGB $d YDXIT $d SILO 050 4 $a Q325.5 $b .A54 2020 082 04 $a 006.31 $2 23 100 1 $a Ameisen, Emmanuel $e author. 245 10 $a Building machine learning powered applications : $b going from idea to product / $c Emmanuel Ameisen. 250 $a First edition. 264 1 $a Sebastopol, CA : $b O'Reilly Media, Inc., $c 2020. 300 $a xvii, 238 pages : $b illustrations ; $c 24 cm 505 0 $a From product goal to ML framing -- Create a plan -- Build your firest end-to-end pipeline -- Acquire an initial dataset -- Train and evaluate your model -- Debug your ML problems -- Using classifiers for writing recommendations -- Considerations when deploying models -- Choose your deployment option -- Build safeguards for models -- Monitor and update models. 520 $a Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers--including experienced practitioners and novices alike--will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you: Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment. 500 $a Includes index. 650 0 $a Machine learning. 650 0 $a Application software $x Development. 650 7 $a Application software $x Development. $2 fast $0 (OCoLC)fst00811707 650 7 $a Machine learning. $2 fast $0 (OCoLC)fst01004795 941 $a 1 952 $l USUX851 $d 20210304012203.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=ABAF56EEA55F11EAA027EF1497128E48 994 $a 92 $b IWAInitiate Another SILO Locator Search