{"product_id":"designing-deep-learning-systems-a-software-engineers-guide-9781633439863","title":"Designing Deep Learning Systems: A Software Engineer's Guide","description":"\u003cb\u003eA vital guide to building the platforms and systems that bring deep learning models to production.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eDesigning Deep Learning Systems\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eTransfer your software development skills to deep learning systems\u003c\/li\u003e \u003cli\u003eRecognize and solve common engineering challenges for deep learning systems\u003c\/li\u003e \u003cli\u003eUnderstand the deep learning development cycle\u003c\/li\u003e \u003cli\u003eAutomate training for models in TensorFlow and PyTorch\u003c\/li\u003e \u003cli\u003eOptimize dataset management, training, model serving and hyperparameter tuning\u003c\/li\u003e \u003cli\u003ePick the right open-source project for your platform\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003eDeep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning's design requirements, \u003ci\u003eDesigning Deep Learning Systems\u003c\/i\u003e is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You'll learn how to build automated and scalable services for core tasks like dataset management, model training\/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting--and lucrative--career as a deep learning engineer. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eDesigning Deep Learning Systems: A software engineer's guide\u003c\/i\u003e teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer's perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you'll need to build your own maintainable, efficient, and scalable deep learning platforms. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe deep learning development cycle\u003c\/li\u003e \u003cli\u003eAutomate training in TensorFlow and PyTorch\u003c\/li\u003e \u003cli\u003eDataset management, model serving, and hyperparameter tuning\u003c\/li\u003e \u003cli\u003eA hands-on deep learning lab\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For software developers and engineering-minded data scientists. Examples in Java and Python. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eChi Wang\u003c\/b\u003e is a principal software developer in the Salesforce Einstein group. \u003cb\u003eDonald Szeto\u003c\/b\u003e was the co-founder and CTO of PredictionIO. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 An introduction to deep learning systems\u003cbr\u003e 2 Dataset management service\u003cbr\u003e 3 Model training service\u003cbr\u003e 4 Distributed training\u003cbr\u003e 5 Hyperparameter optimization service\u003cbr\u003e 6 Model serving design\u003cbr\u003e 7 Model serving in practice\u003cbr\u003e 8 Metadata and artifact store\u003cbr\u003e 9 Workflow orchestration\u003cbr\u003e 10 Path to production\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eChi Wang\u003c\/b\u003e is a principal software developer in the Salesforce Einstein group where he builds the deep learning platform for millions of Salesforce customers. Previously, he worked at Microsoft Bing and Azure on building large-scale distributed systems. Chi has filed six patents, mostly in deep learning systems. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eDonald Szeto\u003c\/b\u003e was the co-founder and CTO of PredictionIO, a startup that aimed to help democratize and accelerate the adoption of machine learning. PredictionIO was acquired by Salesforce, where he continued his work on machine learning and deep learning systems. 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