Machine Learning Engineering in Action - Wilson, Ben
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Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction. In Machine Learning Engineering inAction, you will learn: * Evaluatingdata science problems to find the most effective solution * Scopinga machine learning project for usage expectations and budget * Processtechniques that minimize wasted effort and speed up production * Assessinga project using standardized prototyping work and statistical validation * Choosingthe right technologies and tools for your project * Makingyour codebase more…mehr

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Produktbeschreibung
Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction. In Machine Learning Engineering inAction, you will learn: * Evaluatingdata science problems to find the most effective solution * Scopinga machine learning project for usage expectations and budget * Processtechniques that minimize wasted effort and speed up production * Assessinga project using standardized prototyping work and statistical validation * Choosingthe right technologies and tools for your project * Makingyour codebase more understandable, maintainable, and testable * Automatingyour troubleshooting and logging practices Databricks solutions architect BenWilson lays out an approach to building deployable, maintainable productionmachine learning systems. YouGÇÖll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code!
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Autorenporträt
Ben Wilson has worked as a professional data scientist for more than ten years. He currently works as a resident solutions architect at Databricks,where he focuses on machine learning production architecture with companies ranging from 5-person startups to global Fortune 100. Ben is the creator and lead developer of the Databricks Labs AutoML project, a Scala-and Python-based toolkit that simplifies machine learning feature engineering, model tuning, and pipeline-enabled modelling.