This guide will help you to explore automated machine learning (AutoML), a rapidly growing subfield of machine learning. You'll learn how you can use AutoML to fully automate the machine learning process even if you're not an expert, and in turn increase your productivity drastically.
Über den Autor
Adnan Masood, PhD is a visionary leader practitioner in the field of artificial intelligence, with over 20 years of experience in financial technology and large-scale systems development. He drives the firm's digital transformation, machine learning, and AI strategy. Dr. Masood collaborates with renowned institutions like Stanford AI Lab and MIT CSAIL, holds several patents in AI and machine learning, and is recognized by Microsoft as an AI MVP and Regional Director. In addition to his work in the technology industry, Dr. Masood is a published author, international speaker, STEM robotics coach, and diversity advocate.
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Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologiesnnnKey Features:Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choicenEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsnFind out how you can make machine learning accessible for all users to promote decentralized processesnnnnnBook Deillegalscription:nEvery machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.nnnThis book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.nnnBy the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.nnnWhat You Will Learn:Explore AutoML fundamentals, underlying methods, and techniquesnAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarionFind out the difference between cloud and operations support systems (OSS)nImplement AutoML in enterprise cloud to deploy ML models and pipelinesnBuild explainable AutoML pipelines with transparencynUnderstand automated feature engineering and time series forecastingnAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsnnnnnWho this book is for:nCitizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
