Supercharge your skills for tailoring deep-learning models and deploying them in production environments with ease and precision.
- Learn how to convert a deep learning model running on notebook environments into production-ready application supporting various deployment environments.
- Learn conversion between PyTorch and TensorFlow.
- Achieving satisfactory model performance on various deployment environments where computational powers are often limited.
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.
What you will learn
- Learn how top-tier technology companies carry out a deep learning projects.
- Data preparation, model development & deployment, monitoring & maintenance.
- Convert a proof-of-concept deep learning model into a production-ready application.
- Learn various deep learning libraries like PyTorch / PyTorch Lightning, TensorFlow with and without Keras, TensorFlow with JAX.
- Learn techniques like model pruning and quantization, model distillation & model architecture search.
- Propose the right system architecture for deploying various AI applications at large scale.
- Set up a deep learning pipeline in an efficient and effective way using various AWS services.