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About Machine Learning System Design MEAP V03
Get the big picture and the important details with this end to end guide for designing highly effective reliable Machine Learning systems In Machine Learning System Design With end to end examples you will learnThe big picture of machine learning system designAnalyzing a problem space to identify the optimal ML solutionAce ML system design interviewsSelecting appropriate metrics and evaluation criteriaPrioritizing tasks at different stages of ML system designSolving dataset related problems through data gathering error analysis and feature engineeringRecognizing common pitfalls in ML system developmentDesigning ML systems to be lean maintainable and extensible over timeMachine Learning System Design With end to end examples is a practical guide for planning and designing successful ML applications It lays out a clear repeatable framework for building maintaining and improving systems at any scale Authors Arseny Kravchenko and Valeri Babushkin have filled this unique handbook with campfire stories and personal tips from their own extensive careers You ll learn directly from their experience as you consider every facet of a machine learning system from requirements gathering and data sourcing to deployment and management of the finished system about the technologyMachine learning system design is complex The successful ML engineer needs to navigate a multistep process that demands skills from many different fields and roles This one of kind guide starts by showing you the big picture and then guides you step by step through a framework for creating successful systems You ll learn to excel at delivering for global objectives diving locally into tools and combining your knowledge into an integrated vision about the bookIn Machine Learning System Design With end to end examples you ll find a step by step framework for creating implementing releasing and maintaining your ML system Every part of the life cycle is covered from information gathering to keeping your system well serviced Each stage includes its own handy checklist of requirements and is fully illustrated with real world examples including interesting anecdotes from the author s own careers You ll follow two example companies each building a new ML system exploring how their needs are expressed in design documents and learning best practices by writing your own Along the way you ll learn how to ace ML system design interviews even at highly competitive FAANG like companies and improve existing ML systems by identifying bottlenecks and optimizing system performance about the readerFor readers who know the basics of both software engineering and machine learning Examples in Python about the authorsArseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups including real time video processing manufacturing optimization and financial transactions analysis Valerii Babushkin is an accomplished data science leader with extensive experience in the tech industry He currently serves as the VP of Data Science at Blockchain com where he is responsible for leading the company s data driven initiatives Prior to joining Blockchain com Valerii held key roles at leading tech companies such as Facebook Alibaba and X5 Retail Group
Detailed Information
| Author: | Arseny Kravchenko |
|---|---|
| Publication Year: | 2023 |
| Language: | English |
| File Size: | 3.9 |
| Format: | |
| Price: | FREE |
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