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Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning Frameworks
The book focuses on closing the gap between the theory and the applications with detailed examples. While there are many books on introducing various AI models for different problems, we have seen limited resources on the real difficulty, the deployment. This book is a collection of our knowledge which we have obtained from deploying hundreds of AI-based services at large scale. Having said that, people in the domain of data analytics, typically categorized as data scientists or data engineers, would be the primary audience of this work. However, considering the extreme scale that the book aims to cover, the content would be also insightful for those who need to make a critical decision for the overall system, such as software architects, project and product managers as well as C-level executives.
Since this book puts emphasis on how to deploy machine learning systems at scale, readers with some knowledge in machine learning or software engineering would find the contents easier to follow. However, this is not a hard requirement as we also discuss theoretical and technical background with the assumption that readers are new to the domain. Overall, we believe that our book would be fruitful and interesting to all the readers regardless of their specialties or experiences. This book is fully focused on applied side of Deep Learning (DL). Therefore, we are not introducing theory behind different DL components and DL training process.
The book can be purchased through Amazon or Packt in various formats.
Essential skills for tailoring deep-learning models and deploying them in production environments. You will learn how to create and tailor models based on both TensorFlow (TF) and PyTorch. We present a vast amount of tools commonly used in production environments for a feature extraction, faster training, model understanding and versioning.
Tools:
- TF, PyTorch (DL Frameworks)
- Distributed training: with TF and PyTorch, Horovod, Ray
- AWS EC2, AWS EMR, AWS Sagemaker
- Kubeflow
- Apache Spark
- MLFlow, DVC, Weights and Biases
- Ray Tune
- ELI5, SHAP
Developers will be able to transform models into a desired format and deploy them with a full understanding of tradeoffs and possible alternative approaches. The book provides concrete implementations and associated methodologies that are off-the-shelf allowing readers to apply the knowledge in this book right away without much difficulty.
By the end of this book, you will fully understand how to convert a PoC-like deep learning model into a ready-to-use version that is suitable for the target production environment.
- Tomasz (Tom) Palczewski - github - LinkedIn - google scholar
- Brandon Lee - personal web page - github - LinkedIn - google scholar
- Lenin Mookiah - github - LinkedIn - google scholar
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.