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.
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.
Deepak Mukunthu is a product leader with 16+ years of experience.
With his experience in Big data, Analytics and AI, Deepak has
played instrumental leadership roles in transforming organizations
and teams become data driven and adopt machine learning. He brings
a good mix of thought leadership, customer understanding and
innovation to design and deliver compelling products that resonate
well with customers. In his current role of Principal Program
Manager on Automated ML in Azure AI platform group at Microsoft,
Deepak drives product strategy and roadmap for Automated ML with
the goal of accelerating AI for data scientists and democratizing
AI for other personas interested in machine learning. In addition
to shaping the product direction, he also plays an instrumental
role in helping customers adopt Automated ML for their
business-critical scenarios. Prior to joining Microsoft, Deepak
worked at Trilogy where he played multiple roles - Consultant,
Business development, Program manager, Engineering manager -
successfully leading distributed teams across the globe and
managing technical integration of acquisitions.
Parashar Shah works for Microsoft as a Data Scientist, Senior
Program/Product Manager in Azure Machine Learning platform team
within the Cloud + AI Platform organization. His first book,
Hands-On Machine Learning with Azure: Build powerful models with
cognitive machine learning and artificial intelligence, was
published in Nov 2018. Prior to joining Microsoft, he worked for
Alcatel-Lucent/Nokia Networks/Bell Labs where he helped global
telecom operators (across North America, Europe, Middle East and
APAC) as a solution architect/product manager. Parashar has a MBA
from Indian Institute of Management Bangalore & B.E. (E.C.) from
Nirma Institute of Technology, Ahmedabad. He has filed for 5
patents (in published state), he loves to work on new technologies
and ideas. Parashar's experience and interests span across
Artificial Intelligence, Machine Learning, Big Data, Data Science,
Blockchain, Virtual Reality, Internet of Things (IoT), Advanced
Analytics, Mobile application development, Wireless Technologies &
Device Management.
Wee Hyong Tok is part of the AzureCAT team at Microsoft. He has
extensive leadership experience leading multi-disciplinary team of
engineers and data scientists, working on cutting-edge AI
capabilities that are infused into products and services. He is a
tech visionary with a background in product management, machine
learning/deep learning and working on complex engagements with
customers. Over the years, he has demonstrated that his early
thought-leadership white papers on tech trends have become reality,
and deeply integrated into many products. His ability to
strategize, and turn strategy to execution, and hunting for
customer adoption has enabled many projects that he works on to be
successful. He is continuously pushing the boundaries of products
for machine learning and deep learning. His team works extensively
with deep learning frameworks, ranging from TensorFlow, CNTK,
Keras, and PyTorch. Wee Hyong has worn many hats in his career -
developer, program/product manager, data scientist, researcher, and
strategist, and his range of experience has given him unique super
powers to lead and define the strategy for high-performing Data and
AI innovation teams. Throughout his career, he has been a trusted
advisor to the C-suite, from Fortune 500 companies to startups.
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