Deep Learning in Production
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Beschreibung
Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples. 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 Book description Deep Learning 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 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. Table of Contents Designing a machine learning system Setting up a Deep Learning Workstation Writing and Structuring Deep Learning Code Data Processing Training Serving Deploying Scaling Building an End-to-End Pipeline
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