Supercharge your skills for tailoring deep-learning models and deploying them in production environments with ease and precision.
Machine learning engineers, deep learning specialists, and data engineers without extensive experience encounter various problems when moving their models to a production environment.
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.
In this book, you will learn how to construct complex models in PyTorch and TensorFlow deep-learning frameworks. You will acquire knowledge to transform your models from one framework to the other and learn how to tailor them for specific requirements that the deployment setting introduces. 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.
Readers will have hands-on experience with commonly used deep learning frameworks and popular web services designed for data analytics at scale. You will get to grips with our collective know-hows from deploying hundreds of AI-based services at large scale.
Machine learning engineers, deep learning specialists, and data scientists will find this book closing the gap between the theory and the applications with detailed examples. Readers with beginner level knowledge in machine learning or software engineering would find the contents easier to follow.
