Quickly build and deploy machine learning models without managing
infrastructure, and improve productivity using Amazon SageMaker's
capabilities such as Amazon SageMaker Studio, Autopilot,
Experiments, Debugger, and Model Monitor Key Features Build, train,
and deploy machine learning models quickly using Amazon SageMaker
Analyze, detect, and receive alerts relating to various business
problems using machine learning algorithms and techniques Improve
productivity by training and fine-tuning machine learning models in
production Book DescriptionAmazon SageMaker enables you to quickly
build, train, and deploy machine learning (ML) models at scale,
without managing any infrastructure. It helps you focus on the ML
problem at hand and deploy high-quality models by removing the
heavy lifting typically involved in each step of the ML process.
This book is a comprehensive guide for data scientists and ML
developers who want to learn the ins and outs of Amazon SageMaker.
You'll understand how to use various modules of SageMaker as a
single toolset to solve the challenges faced in ML. As you
progress, you'll cover features such as AutoML, built-in algorithms
and frameworks, and the option for writing your own code and
algorithms to build ML models. Later, the book will show you how to
integrate Amazon SageMaker with popular deep learning libraries
such as TensorFlow and PyTorch to increase the capabilities of
existing models. You'll also learn to get the models to production
faster with minimum effort and at a lower cost. Finally, you'll
explore how to use Amazon SageMaker Debugger to analyze, detect,
and highlight problems to understand the current model state and
improve model accuracy. 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 Create and automate
end-to-end machine learning workflows on Amazon Web Services (AWS)
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 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. Some understanding
of machine learning concepts and the Python programming language
will also be beneficial.
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