_PDF_ Deep Learning in Production
COPY LINK: https://pdf.bookcenterapp.com/yumpu/6180033773 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 codeHow to unit test and debug Machine Learning codeHow to build and deploy efficient data pipelinesHow to serve Deep Learning modelsHow to deploy and scale your applicationWhat is MLOps and how to build end-to-end pipelinesWho is this book for?Software engineers who are starting out with deep learningMachine learning researchers with limited software engineering backgroundMachine learning engineers who seek to strengthen their knowledgeData scientists who want to productionize their models and build customer-facing applicationsWhat tools you will use?Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AIBook descriptionDeep 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 dep
COPY LINK: https://pdf.bookcenterapp.com/yumpu/6180033773
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 codeHow to unit test and debug Machine Learning codeHow to build and deploy efficient data pipelinesHow to serve Deep Learning modelsHow to deploy and scale your applicationWhat is MLOps and how to build end-to-end pipelinesWho is this book for?Software engineers who are starting out with deep learningMachine learning researchers with limited software engineering backgroundMachine learning engineers who seek to strengthen their knowledgeData scientists who want to productionize their models and build customer-facing applicationsWhat tools you will use?Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AIBook descriptionDeep 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 dep
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Deep Learning in Production
DESCRIPTION
COPY LINK: https://pdf.bookcenterapp.com/yumpu/6180033773 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 codeHow to unit test and
debug Machine Learning codeHow to build and deploy efficient data pipelinesHow to serve Deep
Learning modelsHow to deploy and scale your applicationWhat is MLOps and how to build end-toend
pipelinesWho is this book for?Software engineers who are starting out with deep
learningMachine learning researchers with limited software engineering backgroundMachine
learning engineers who seek to strengthen their knowledgeData scientists who want to
productionize their models and build customer-facing applicationsWhat tools you will
use?Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud,
Vertex AIBook descriptionDeep 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 ContentsDesigning a machine learning systemSetting up a
Deep Learning WorkstationWriting and Structuring Deep Learning CodeData
ProcessingTrainingServingDeployingScalingBuilding an End-to-End Pipeline
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