Machine Learning Applications In Software Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Applications In Software Engineering write by Du Zhang. This book was released on 2005-02-21. Machine Learning Applications In Software Engineering available in PDF, EPUB and Kindle. Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.
Advances in Machine Learning Applications in Software Engineering
Advances in Machine Learning Applications in Software Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Machine Learning Applications in Software Engineering write by Zhang, Du. This book was released on 2006-10-31. Advances in Machine Learning Applications in Software Engineering available in PDF, EPUB and Kindle. "This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.
Machine Learning Applications in Software Engineering
Machine Learning Applications in Software Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Applications in Software Engineering write by Du Zhang. This book was released on 2005. Machine Learning Applications in Software Engineering available in PDF, EPUB and Kindle. A collection of previously published articles from a variety of publications.
Machine Learning Engineering in Action
Machine Learning Engineering in Action - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Engineering in Action write by Ben Wilson. This book was released on 2022-05-17. Machine Learning Engineering in Action available in PDF, EPUB and Kindle. 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. 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.
Building Machine Learning Powered Applications
Building Machine Learning Powered Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Building Machine Learning Powered Applications write by Emmanuel Ameisen. This book was released on 2020-01-21. Building Machine Learning Powered Applications available in PDF, EPUB and Kindle. Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment
