Swiftly build and deploy machine learning models without managing
infrastructure and boost productivity using the latest Amazon
SageMaker capabilities such as Studio, Autopilot, Data Wrangler,
Pipelines, and Feature Store Key Features Build, train, and deploy
machine learning models quickly using Amazon SageMaker Optimize the
accuracy, cost, and fairness of your models Create and automate
end-to-end machine learning workflows on Amazon Web Services (AWS)
Book DescriptionAmazon SageMaker enables you to quickly build,
train, and deploy machine learning models at scale without managing
any infrastructure. It helps you focus on the machine learning
problem at hand and deploy high-quality models by eliminating the
heavy lifting typically involved in each step of the ML process.
This second edition will help data scientists and ML developers to
explore new features such as SageMaker Data Wrangler, Pipelines,
Clarify, Feature Store, and much more. You'll start by learning how
to use various capabilities of SageMaker as a single toolset to
solve ML challenges and progress to cover features such as AutoML,
built-in algorithms and frameworks, and writing your own code and
algorithms to build ML models. The book will then show you how to
integrate Amazon SageMaker with popular deep learning libraries,
such as TensorFlow and PyTorch, to extend the capabilities of
existing models. You'll also see how automating your workflows can
help you get to production faster with minimum effort and at a
lower cost. Finally, you'll explore SageMaker Debugger and
SageMaker Model Monitor to detect quality issues in training and
production. By the end of this Amazon book, you'll be able to use
Amazon SageMaker on the full spectrum of ML workflows, from
experimentation, training, and monitoring to scaling, deployment,
and automation. What you will learn Become well-versed with data
annotation and preparation techniques Use AutoML features to build
and train machine learning models with AutoPilot Create models
using built-in algorithms and frameworks and your own code Train
computer vision and natural language processing (NLP) models using
real-world examples Cover training techniques for scaling, model
optimization, model debugging, and cost optimization Automate
deployment tasks in a variety of configurations using SDK and
several automation tools Who this book is forThis book is for
software engineers, machine learning developers, data scientists,
and AWS users who are new to using Amazon SageMaker and want to
build high-quality machine learning models without worrying about
infrastructure. Knowledge of AWS basics is required to grasp the
concepts covered in this book more effectively. A solid
understanding of machine learning concepts and the Python
programming language will also be beneficial.
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