Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives.
First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors’ collective knowledge of deploying hundreds of AI-based services at a large scale.
By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting.

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Spis treści

Production-Ready Applied Deep Learning. Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks eBook -- spis treści

• 1. Effective Planning of Deep Learning-Driven Projects
• 2. Data Preparation for Deep Learning Projects
• 3. Developing a Powerful Deep Learning Model
• 4. Experiment Tracking, Model Management, and Dataset Versioning
• 5. Data Preparation in the Cloud
• 6. Efficient Model Training
• 7. Revealing the Secret of Deep Learning Models
• 8. Simplifying Deep Learning Model Deployment
• 9. Scaling a Deep Learning Pipeline
• 10. Improving Inference Efficiency
• 11. Deep Learning on Mobile Devices
• 12. Monitoring Deep Learning Endpoints in Production
• 13. Reviewing the Completed Deep Learning Project