Learning Engineering Practice

This book explains engineering practice, what engineers actually do in their work. The first part explains how to find paid engineering work and prepare for an engineering career. The second part explains the fundamentals of engineering practice, including how to gain access to technical knowledge, how to gain the willing collaboration of other people to make things happen, and how to work safely in hazardous environments. Other chapters explain engineering aspects of project management missed in most courses, how to create commercial value from engineering work and estimate costs, and how to navigate cultural complexities successfully. Later chapters provide guidance on sustainability, time management and avoiding the most common frustrations encountered by engineers at work. This book has been written for engineering students, graduates and novice engineers. Supervisors, mentors and human resources professionals will also find the book helpful to guide early-career engineers and assess their progress. Engineering schools will find the book helpful to help students prepare for professional internships and also for creating authentic practice and assessment exercises.

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 volume aims to provide the reader with a broad cross-section of empirical research being carried out into engineers at work. The chapters provide pointers to other relevant studies over recent decades an important aspect, we believe, because this area has only recently begun to coalesce as a field of study and up to now relevant empirical re

This book is a broad discussion covering the entire software development lifecycle. It uses a comprehensive case study to address each topic and features the following: A description of the development, by the fictional company Homeowner, of the DigitalHome (DH) System, a system with "smart" devices for controlling home lighting, temperature, humidity, small appliance power, and security A set of scenarios that provide a realistic framework for use of the DH System material Just-in-time training: each chapter includes mini tutorials introducing various software engineering topics that are discussed in that chapter and used in the case study A set of case study exercises that provide an opportunity to engage students in software development practice, either individually or in a team environment. Offering a new approach to learning about software engineering theory and practice, the text is specifically designed to: Support teaching software engineering, using a comprehensive case study covering the complete software development lifecycle Offer opportunities for students to actively learn about and engage in software engineering practice Provide a realistic environment to study a wide array of software engineering topics including agile development Software Engineering Practice: A Case Study Approach supports a student-centered, "active" learning style of teaching. The DH case study exercises provide a variety of opportunities for students to engage in realistic activities related to the theory and practice of software engineering. The text uses a fictitious team of software engineers to portray the nature of software engineering and to depict what actual engineers do when practicing software engineering. All the DH case study exercises can be used as team or group exercises in collaborative learning. Many of the exercises have specific goals related to team building and teaming skills. The text also can be used to support the professional development or certification of practicing software engineers. The case study exercises can be integrated with presentations in a workshop or short course for professionals.

Shows how the engineering curriculum can be a site for rendering social justice visible in engineering, for exploring complex socio-technical interplays inherent in engineering practice, and for enhancing teaching and learning Using social justice as a catalyst for curricular transformation, Engineering Justice presents an examination of how politics, culture, and other social issues are inherent in the practice of engineering. It aims to align engineering curricula with socially just outcomes, increase enrollment among underrepresented groups, and lessen lingering gender, class, and ethnicity gaps by showing how the power of engineering knowledge can be explicitly harnessed to serve the underserved and address social inequalities. This book is meant to transform the way educators think about engineering curricula through creating or transforming existing courses to attract, retain, and motivate engineering students to become professionals who enact engineering for social justice. Engineering Justice offers thought-provoking chapters on: why social justice is inherent yet often invisible in engineering education and practice; engineering design for social justice; social justice in the engineering sciences; social justice in humanities and social science courses for engineers; and transforming engineering education and practice. In addition, this book: Provides a transformative framework for engineering educators in service learning, professional communication, humanitarian engineering, community service, social entrepreneurship, and social responsibility Includes strategies that engineers on the job can use to advocate for social justice issues and explain their importance to employers, clients, and supervisors Discusses diversity in engineering educational contexts and how it affects the way students learn and develop Engineering Justice is an important book for today’s professors, administrators, and curriculum specialists who seek to produce the best engineers of today and tomorrow.

Blended Learning combines the conventional face-to-face course delivery with an online component. The synergetic effect of the two modalities has proved to be of superior didactic value to each modality on its own. The highly improved interaction it offers to students, as well as direct accessibility to the lecturer, adds to the hitherto unparalleled learning outcomes. "Blended Learning in Engineering Education: Recent Developments in Curriculum, Assessment and Practice" highlights current trends in Engineering Education involving face-to-face and online curriculum delivery. This book will be especially useful to lecturers and postgraduate/undergraduate students as well as university administrators who would like to not only get an up-to-date overview of contemporary developments in this field, but also help enhance academic performance at all levels.

This book has been developed with an intellectual framework to focus on the challenges and specific qualities applicable to graduates on the threshold of their careers. Young professionals have to establish their competence in complying with multifaceted sets of ethical, environmental, social, and technological parameters. This competence has a vital impact on the curricula of higher education programs, because professional bodies today rely on accredited degrees as the main route for membership. Consequently, this four-part book makes a suitable resource for a two-semester undergraduate course in professional practice and career development in universities and colleges. With its comprehensive coverage of a large variety of topics, each part of the book can be used as a reference for other related courses where sustainability, leadership, systems thinking and professional practice are evident and increasingly visible. Features Identifies the values that are unique to the engineering and computing professions, and promotes a general understanding of what it means to be a member of a profession Explains how ethical and legal considerations play a role in engineering practice Discusses the importance of professional communication and reflective practice to a range of audiences Presents the practices of leadership, innovation, entrepreneurship, safety and sustainability in engineering design Analyzes and discusses the contemporary practices of project management, artificial intelligence, and professional career development.

An immense treasure trove containing hundreds of equipment symptoms, arranged so as to allow swift identification and elimination of the causes. These rules of thumb are the result of preserving and structuring the immense knowledge of experienced engineers collected and compiled by the author - an experienced engineer himself - into an invaluable book that helps younger engineers find their way from symptoms to causes. This sourcebook is unrivalled in its depth and breadth of coverage, listing five important aspects for each piece of equipment: * area of application * sizing guidelines * capital cost including difficult-to-find installation factors * principles of good practice, and * good approaches to troubleshooting. Extensive cross-referencing takes into account that some items of equipment are used for many different purposes, and covers not only the most familiar types, but special care has been taken to also include less common ones. Consistent terminology and SI units are used throughout the book, while a detailed index quickly and reliably directs readers, thus aiding engineers in their everyday work at chemical plants: from keywords to solutions in a matter of minutes.