- eBook:Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
- Author:Dr. Adnan Masood
- Data:March 9, 2021
- Pages:235 pages
- Format:PDF, ePUB
- Get up to speed with AutoML using the platform of your choice, such as OSS, Azure, AWS, or GCP
- Eliminate mundane tasks in data engineering and reduce human errors in ML models that occur mainly due to manual steps
- Make machine learning accessible for all users, helping promote a decentralized process
Book DescriptionEvery 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.
This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and more. You'll explore different ways of implementing these techniques in open-source tools. Next, you'll focus on enterprise tools, learning different ways of implementing AutoML in three major cloud service providers, including Microsoft Azure, Amazon Web Services (AWS), and the Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. Later chapters will show you how to develop accurate models by automating time-consuming and repetitive tasks involved in the machine learning development lifecycle.
By the end of this book, you'll be able to build and deploy automated machine learning models that are not only accurate, but also increase productivity, allow interoperability, and minimize featuring engineering tasks.
What you will learn
- Explore AutoML fundamentals, underlying methods, and techniques
- Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario and differentiate between cloud and OSS offerings
- Implement AutoML in tools such as AWS, Azure, and GCP and while deploying ML models and pipelines
- Build explainable AutoML pipelines with transparency
- Understand automated feature engineering and time series forecasting
- Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems
Who This Book Is ForCitizen 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, Amazon Web Services (AWS), and Google Cloud Platform will find this book useful.
Chapter 1: A Lap around Automated Machine Learning
Chapter 2: Automated Machine Learning, Algorithms, and Techniques
Chapter 3: Automated Machine Learning with Open Source Tools and Libraries
Section 2: AutoML with Cloud Platforms
Chapter 4: Getting Started with Azure Machine Learning
Chapter 5: Automated Machine Learning with Microsoft Azure
Chapter 6: Machine Learning with AWS
Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot
Chapter 8: Machine Learning with Google Cloud Platform
Chapter 9: Automated Machine Learning with GCP
Section 3: Applied Automated Machine Learning
Chapter 10: AutoML in the Enterprise