{"product_id":"machine-learning-infrastructure-and-best-practices-for-software-engineers-take-your-machine-learning-software-from-a-prototype-to-a-fully-fledged-sof-paperback","title":"Machine Learning Infrastructure and Best Practices for Software Engineers: Take your machine learning software from a prototype to a fully fledged sof - Paperback","description":"\u003cp\u003eby \u003cb\u003eMiroslaw Staron\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eEfficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eLearn how to scale-up your machine learning software to a professional level\u003c\/li\u003e\n\u003cli\u003eSecure the quality of your machine learning pipeline at runtime\u003c\/li\u003e\n\u003cli\u003eApply your knowledge to natural languages, programming languages, and images\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products.\u003c\/p\u003e\u003cp\u003eThe book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you'll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality.\u003c\/p\u003e\u003cp\u003eTowards the end, you'll address the most challenging aspect of large-scale machine learning systems - ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began - large-scale machine learning software.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eIdentify what the machine learning software best suits your needs\u003c\/li\u003e\n\u003cli\u003eWork with scalable machine learning pipelines\u003c\/li\u003e\n\u003cli\u003eScale up pipelines from prototypes to fully fledged software\u003c\/li\u003e\n\u003cli\u003eChoose suitable data sources and processing methods for your product\u003c\/li\u003e\n\u003cli\u003eDifferentiate raw data from complex processing, noting their advantages\u003c\/li\u003e\n\u003cli\u003eTrack and mitigate important ethical risks in machine learning software\u003c\/li\u003e\n\u003cli\u003eWork with testing and validation for machine learning systems\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you're a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eMachine Learning Compared to Traditional Software\u003c\/li\u003e\n\u003cli\u003eElements of a Machine Learning Software System\u003c\/li\u003e\n\u003cli\u003eData in Software Systems - Text, Images, Code, Features\u003c\/li\u003e\n\u003cli\u003eData Acquisition, Data Quality and Noise\u003c\/li\u003e\n\u003cli\u003eQuantifying and Improving Data Properties\u003c\/li\u003e\n\u003cli\u003eTypes of Data in ML Systems\u003c\/li\u003e\n\u003cli\u003eFeature Engineering for Numerical and Image Data\u003c\/li\u003e\n\u003cli\u003eFeature Engineering for Natural Language Data\u003c\/li\u003e\n\u003cli\u003eTypes of Machine Learning Systems - Feature-Based and Raw Data Based (Deep Learning)\u003c\/li\u003e\n\u003cli\u003eTraining and evaluation of classical ML systems and neural networks\u003c\/li\u003e\n\u003cli\u003eTraining and evaluation of advanced algorithms - deep learning, autoencoders, GPT-3\u003c\/li\u003e\n\u003cli\u003eDesigning machine learning pipelines (MLOps) and their testing\u003c\/li\u003e\n\u003cli\u003eDesigning and implementation of large scale, robust ML software - a comprehensive example\u003c\/li\u003e\n\u003cli\u003eEthics in data acquisition and management\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 346\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.72 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 31, 2024\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42702743306303,"sku":"9781837634064","price":77.74,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/a8c7de67fae59f60d8c24d3aa4f6410f_a8cb4f9d-8cc0-47c2-9082-9a75107fdaa1.jpg?v=1765026119","url":"https:\/\/dhlswag.com\/products\/machine-learning-infrastructure-and-best-practices-for-software-engineers-take-your-machine-learning-software-from-a-prototype-to-a-fully-fledged-sof-paperback","provider":"BBB","version":"1.0","type":"link"}