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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.
Software Engineer's Guide to Deep Learning System Design is a practical guide for software engineers and data scientists who are designing and building platforms for Deep Learning (DL). It’s full of hands-on examples that will help you transfer your software development skills to implementing Deep Learning platforms.
In Software Engineer's Guide to Deep Learning System Design, 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.
An introduction to deep learning systems
Dataset management service
Model training service
Distributed training
Hyperparameter optimization (HPO) service
Model serving design
Model serving in practice
Metadata and artifact store
Workflow orchestration
Path to production
Appendix A A hello world deep learning system
Appendix B Survey of existing solutions
Appendix C Creating an HPO service with Kubeflow Katib