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Engineering Deep Learning Systems: A Software Engineer's Guide

Description:

Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models.

In
Engineering Deep Learning Systems you will learn how to:

  • Transfer your software development skills to deep learning systems
  • Recognize and solve common engineering challenges for deep learning systems
  • Understand the deep learning development cycle
  • Automate training for models in TensorFlow and PyTorch
  • Optimize dataset management, training, model serving and hyperparameter tuning
  • Pick the right open-source project for your platform
Engineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It's full of hands-on examples that will help you transfer your software development skills to implementing 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.

about the technology

Behind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system's platform differs from other distributed systems. By mastering the core ideas in this book, you'll be able to support deep learning systems in a way that's fast, repeatable, and reliable.


From the Back Cover

Engineering Deep Learning Systems teaches you to design and implement an automated platform to support creating, training, and maintaining deep learning models. In it, you'll learn just enough about deep learning to understand the needs of the data scientists who will be using your system. You'll learn to gather requirements, translate them into system component design choices, and integrate those components into a cohesive whole. A complete example system and insightful exercises help you build an intuitive understanding of DL system design.

About the Author

Chi Wang 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.

Donald Szeto 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. Donald is currently investing in, advising, and mentoring technology startups.

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Engineering Deep Learning Systems: A Software Engineer's Guide

Product ID: K1633439860
Condition: New

4.6

AED26411

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Type: Paperback
Availability: In Stock

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Order today to get by 7-14 business days

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Engineering Deep Learning Systems: A Software Engineer's Guide

Product ID: K1633439860
Condition: New

4.6

Engineering Deep Learning Systems: A Software Engineer's Guide-0
Type: Paperback

AED26411

Price includes VAT & Import Duties
Availability: In Stock

Quantity:

|

Order today to get by 7-14 business days

This item qualifies for free delivery

Returns & Warranty policies

Imported From: United Kingdom

At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.

BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.

Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.

If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.

Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.

All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.

While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.

Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.

BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.

All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.

All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.

Description:

Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models.

In
Engineering Deep Learning Systems you will learn how to:

  • Transfer your software development skills to deep learning systems
  • Recognize and solve common engineering challenges for deep learning systems
  • Understand the deep learning development cycle
  • Automate training for models in TensorFlow and PyTorch
  • Optimize dataset management, training, model serving and hyperparameter tuning
  • Pick the right open-source project for your platform
Engineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It's full of hands-on examples that will help you transfer your software development skills to implementing 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.

about the technology

Behind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system's platform differs from other distributed systems. By mastering the core ideas in this book, you'll be able to support deep learning systems in a way that's fast, repeatable, and reliable.


From the Back Cover

Engineering Deep Learning Systems teaches you to design and implement an automated platform to support creating, training, and maintaining deep learning models. In it, you'll learn just enough about deep learning to understand the needs of the data scientists who will be using your system. You'll learn to gather requirements, translate them into system component design choices, and integrate those components into a cohesive whole. A complete example system and insightful exercises help you build an intuitive understanding of DL system design.

About the Author

Chi Wang 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.

Donald Szeto 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. Donald is currently investing in, advising, and mentoring technology startups.

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