Название: Deep Learning in ProductionАвтор: Sergios Karagiannakos
Издательство: Leanpub
Год: 2022-02-07
Страниц: 223
Язык: английский
Формат: pdf (true), epub
Размер: 58.0 MB
Build, train, deploy, scale and maintain Deep Learning models. Understand ML infrastructure and MLOps using hands-on examples.
Deep Learning (DL) research is advancing rapidly over the past years. Frameworks and libraries are constantly been developed and updated. However, we still lack standardized solutions on how to serve, deploy and scale Deep Learning models. Deep Learning infrastructure is not very mature yet.
This book accumulates a set of best practices and approaches on how to build robust and scalable Machine Learning (ML) applications. It covers the entire lifecycle from data processing and training to deployment and maintenance. It will help you understand how to transfer methodologies that are generally accepted and applied in the software community, into Deep Learning projects.
It's an excellent choice for researchers with a minimal software background, software engineers with little experience in Machine Learning, or aspiring Machine Learning engineers.
What is the book’s goal?
If I were to summarize the book into 4 sentences, I’d say that you will learn:
• how to structure and develop production-ready machine learning code
• how to optimize the model’s performance and memory requirements
• how to make it available to the public by setting up a service on the cloud
• how to scale and maintain the service as the user base grows
What you will learn?
Best practices to write Deep Learning code
How to unit test and debug Machine Learning code
How to build and deploy efficient data pipelines
How to serve Deep Learning models
How to deploy and scale your application
What is MLOps and how to build end-to-end pipelines
Who is this book for?
Software engineers who are starting out with Deep Learning
Machine learning researchers with limited software engineering background
Machine learning engineers who seek to strengthen their knowledge
Data scientists who want to productionize their models and build customer-facing applications
What tools you will use?
Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AI
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