Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. --

​This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.

Master Machine Learning Fundamentals Whether you’re an aspiring data scientist, business professional, or curious learner, Machine Learning Fundamentals in Action is your essential guide to the world of machine learning. Packed with practical examples and real-world applications, this book helps you navigate key concepts and techniques transforming industries today. Unlock the Power of Machine Learning Discover every step, from data preparation to building and deploying models, with clear and actionable insights. Who Is This Book For? Aspiring Data Scientists: Build a solid foundation in ML concepts. Business Professionals: Use data-driven decisions to solve challenges. Developers and Engineers: Get hands-on experience with model-building techniques. Curious Learners: Understand ML with easy, step-by-step explanations. What You’ll Learn: Core ML principles and real-world applications Types of ML: Supervised, Unsupervised, and Reinforcement Learning Advanced topics: Neural networks, deep learning, and more How to deploy models and avoid common pitfalls Start your machine learning journey today!

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You’ll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You’ll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author’s extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it’s so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

This book identifies Artificial Intelligence (AI) as a growing field that is being incorporated into many aspects of human life, including healthcare practice and delivery. The precision, automation, and potential of AI brings multiple benefits to the way disease is diagnosed, investigated and treated. Currently, there is a lack of any appreciable understanding of AI and this book provides detailed understandings, which include; foundational concepts, current applications, future challenges amongst most healthcare practitioners. The book is divided into four sections: basic concepts, current applications, limitations and future directions. Each section is comprised of chapters written by expert academics, researchers and practitioners at the intersection between AI and medicine. The purpose of the book is to promote AI literacy as an important component of modern medical practice. This book is suited for all readers as it requires no previous knowledge, it walks non-technical clinicians through the complex ideas and concepts in an easy to understand manner.

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 (AutoML), 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 ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every 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 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. By 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. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen 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.

Build your company’s next-generation growth strategy by using emerging technologies to disrupt your field and energize your business In Digital Operating Model: The Future of Business, digital strategist and execution expert Rajesh Sinha delivers a robust and practical operating blueprint for digital transformation. Applicable to any industry, any size company, this playbook helps executives, professionals, managers, founders, owners, and other business leaders understand the importance and realize the benefits of a digital future for their companies—all without having to spend massive amounts of money in the process. The author explores effective methods to create multiple digital accelerators, develop cultural alignment that fosters innovation and delivers rapid solutions, and shares insights into the new mantras of our goods-and-services on-demand economy. Readers will also find: Step-by-step guidance to implementing a digital platform strategy that leads to exponential business growth Methods for designing and applying new businesses processes that create better experiences internally for your teams and externally for your customers and customers’ customers, which also leads to exponential business growth Real-life examples and case studies of businesses that have achieved successful digital acceleration and grown dramatically in the process Digital Operating Model shows readers how to meet their professional objectives while realizing profound transformation that offers innovative and durable differentiation both in terms of purpose and profits.

This volume presents an international group of researchers who model animal and human behavior--both simple and complex. The models presented focus on such subjects as the pattern of eating in meals and bouts, the energizing and shaping impact of reinforcers on behavior, transitive inferential reasoning, responding to a compound stimulus, avoidance and escape learning, recognition memory, category formation, generalization, the timing of adaptive responses, and chromosomes exchanging information. The chapters are united by a common interest in adaptive behavior--whether of human, animal, or artificial system--and clearly demonstrate the rich variety of ways in which this fascinating area of research can be approached. In so doing, the book demonstrates the range of thought that qualifies as theorizing in the contemporary study of the mechanisms of adaptive behavior. It has two purposes: to bring together a very wide range of approaches in one place and to give authors space to explain how their ideas developed. Journal literature often presents fully-formed theories with no explanation of how an idea came to have the shape in which it is presented. In this volume, however, leaders in different fields provide background on the development of their ideas. Where once psychologists and a few zoologists had this field to themselves, now various types of computer scientists have added great energy to the mix.