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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.
Work through interesting real-life business use cases to uncover
valuable insights from unstructured text using AWS AI services Key
Features Get to grips with AWS AI services for NLP and find out how
to use them to gain strategic insights Run Python code to use
Amazon Textract and Amazon Comprehend to accelerate business
outcomes Understand how you can integrate human-in-the-loop for
custom NLP use cases with Amazon A2I Book DescriptionNatural
language processing (NLP) uses machine learning to extract
information from unstructured data. This book will help you to move
quickly from business questions to high-performance models in
production. To start with, you'll understand the importance of NLP
in today's business applications and learn the features of Amazon
Comprehend and Amazon Textract to build NLP models using Python and
Jupyter Notebooks. The book then shows you how to integrate AI in
applications for accelerating business outcomes with just a few
lines of code. Throughout the book, you'll cover use cases such as
smart text search, setting up compliance and controls when
processing confidential documents, real-time text analytics, and
much more to understand various NLP scenarios. You'll deploy and
monitor scalable NLP models in production for real-time and batch
requirements. As you advance, you'll explore strategies for
including humans in the loop for different purposes in a document
processing workflow. Moreover, you'll learn best practices for
auto-scaling your NLP inference for enterprise traffic. Whether
you're new to ML or an experienced practitioner, by the end of this
NLP book, you'll have the confidence to use AWS AI services to
build powerful NLP applications. What you will learn Automate
various NLP workflows on AWS to accelerate business outcomes Use
Amazon Textract for text, tables, and handwriting recognition from
images and PDF files Gain insights from unstructured text in the
form of sentiment analysis, topic modeling, and more using Amazon
Comprehend Set up end-to-end document processing pipelines to
understand the role of humans in the loop Develop NLP-based
intelligent search solutions with just a few lines of code Create
both real-time and batch document processing pipelines using Python
Who this book is forIf you're an NLP developer or data scientist
looking to get started with AWS AI services to implement various
NLP scenarios quickly, this book is for you. It will show you how
easy it is to integrate AI in applications with just a few lines of
code. A basic understanding of machine learning (ML) concepts is
necessary to understand the concepts covered. Experience with
Jupyter notebooks and Python will be helpful.
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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