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Develop smart applications without spending days and weeks building
machine-learning models. With this practical book, you'll learn how
to apply Automated Machine Learning, a process that uses machine
learning to help people build machine learning models. Deepak
Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of
technical depth, hands-on examples, and case studies that show how
customers are solving real-world problems with this technology.
Building machine learning models is an iterative and time-consuming
process. Even those who know how to create these models may be
limited in how much they can explore. Once you complete this book,
you'll understand how to apply Automated Machine Learning to your
data right away. Learn how companies in different industries are
benefiting from Automated Machine Learning Get started with
Automated Machine Learning using Azure Explore aspects such as
algorithm selection, auto featurization, and hyperparameter tuning
Understand how data analysts, BI professionals, and developers can
use Automated Machine Learning in their familiar tools and
experiences Learn how to get started using Automated Machine
Learning for use cases including classification and regression.
Implement machine learning, cognitive services, and artificial
intelligence solutions by leveraging Azure cloud technologies Key
Features Learn advanced concepts in Azure ML and the Cortana
Intelligence Suite architecture Explore ML Server using SQL Server
and HDInsight capabilities Implement various tools in Azure to
build and deploy machine learning models Book
DescriptionImplementing Machine learning (ML) and Artificial
Intelligence (AI) in the cloud had not been possible earlier due to
the lack of processing power and storage. However, Azure has
created ML and AI services that are easy to implement in the cloud.
Hands-On Machine Learning with Azure teaches you how to perform
advanced ML projects in the cloud in a cost-effective way. The book
begins by covering the benefits of ML and AI in the cloud. You will
then explore Microsoft's Team Data Science Process to establish a
repeatable process for successful AI development and
implementation. You will also gain an understanding of AI
technologies available in Azure and the Cognitive Services APIs to
integrate them into bot applications. This book lets you explore
prebuilt templates with Azure Machine Learning Studio and build a
model using canned algorithms that can be deployed as web services.
The book then takes you through a preconfigured series of virtual
machines in Azure targeted at AI development scenarios. You will
get to grips with the ML Server and its capabilities in SQL and
HDInsight. In the concluding chapters, you'll integrate patterns
with other non-AI services in Azure. By the end of this book, you
will be fully equipped to implement smart cognitive actions in your
models. What you will learn Discover the benefits of leveraging the
cloud for ML and AI Use Cognitive Services APIs to build
intelligent bots Build a model using canned algorithms from
Microsoft and deploy it as a web service Deploy virtual machines in
AI development scenarios Apply R, Python, SQL Server, and Spark in
Azure Build and deploy deep learning solutions with CNTK, MMLSpark,
and TensorFlow Implement model retraining in IoT, Streaming, and
Blockchain solutions Explore best practices for integrating ML and
AI functions with ADLA and logic apps Who this book is forIf you
are a data scientist or developer familiar with Azure ML and
cognitive services and want to create smart models and make sense
of data in the cloud, this book is for you. You'll also find this
book useful if you want to bring powerful machine learning services
into your cloud applications. Some experience with data
manipulation and processing, using languages like SQL, Python, and
R, will aid in understanding the concepts covered in this book
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Nadine Gordimer
Paperback
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R398
R330
Discovery Miles 3 300
Not available
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