| Description |
xx, 337 pages : illustrations ; 24 cm |
| Note |
Includes index |
| Contents |
An introduction to deep learning systems -- Dataset management service -- Model training service -- Distributed training -- Hyperparameter optimization service -- Model serving design -- Model serving in practice -- Metadata and artifact store -- Workflow orchestration -- Path to production |
| Summary |
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. This book gives you that depth. Designing deep learning systems : a guide for software engineers 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 majot 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 |
| Subject |
Software engineering.
|
|
Deep learning (Machine learning)
|
| Added Author |
Szeto, Donald, author.
|
|
Savarese, Silvio, writer of foreword.
|
|
Xiong, Caiming, writer of foreword.
|
| Added Title |
Guide for software engineers |
| ISBN |
9781633439863 |
|
1633439860 |
|
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