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Book Cover
E-book
Author Staron, Miroslaw, author.

Title Machine Learning Infrastructure and Best Practices for Software Engineers : Take Your Machine Learning Software from a Prototype to a Fully Fledged Software System / Miroslaw Staron)
Published Birmingham : Packt Publishing, Limited, 2024

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Description 1 online resource (346 p.) : illustrations
Contents Cover -- Title page -- Copyright and credits -- Dedication -- Contributors -- Table of contents -- Preface -- Part 1: Machine Learning Landscape in Software Engineering -- Bookmark 170 -- Chapter 1: Machine Learning Compared to Traditional Software -- Machine learning is not traditional software -- Supervised, unsupervised, and reinforcement learning -- it is just the beginning -- An example of traditional and machine learning software -- Probability and software -- how well they go together -- Testing and evaluation -- the same but different -- Summary -- References
Chapter 2: Elements of a Machine Learning System -- Elements of a production machine learning system -- Data and algorithms -- Data collection -- Feature extraction -- Data validation -- Configuration and monitoring -- Configuration -- Monitoring -- Infrastructure and resource management -- Data serving infrastructure -- Computational infrastructure -- How this all comes together -- machine learning pipelines -- References -- Chapter 3: Data in Software Systems -- Text, Images, Code, and Their Annotations -- Raw data and features -- what are the differences? -- Images -- Text
Visualization of output from more advanced text processing -- Structured text -- source code of programs -- Every data has its purpose -- annotations and tasks -- Annotating text for intent recognition -- Where different types of data can be used together -- an outlook on multi-modal data models -- References -- Chapter 4: Data Acquisition, Data Quality, and Noise -- Sources of data and what we can do with them -- Extracting data from software engineering tools -- Gerrit and Jira -- Extracting data from product databases -- GitHub and Git -- Data quality -- Noise -- Summary -- References
Chapter 5: Quantifying and Improving Data Properties -- Feature engineering -- the basics -- Clean data -- Noise in data management -- Attribute noise -- Splitting data -- How ML models handle noise -- References -- Part 2: Data Acquisition and Management -- Chapter 6: Processing Data in Machine Learning Systems -- Numerical data -- Summarizing the data -- Diving deeper into correlations -- Summarizing individual measures -- Reducing the number of measures -- PCA -- Other types of data -- images -- Text data -- Toward feature engineering -- References
Chapter 7: Feature Engineering for Numerical and Image Data -- Feature engineering -- Feature engineering for numerical data -- PCA -- t-SNE -- ICA -- Locally linear embedding -- Linear discriminant analysis -- Autoencoders -- Feature engineering for image data -- Summary -- References -- Chapter 8: Feature Engineering for Natural Language Data -- Natural language data in software engineering and the rise of Github Copilot -- What a tokenizer is and what it does -- Bag-of-words and simple tokenizers -- WordPiece tokenizer -- BPE -- The SentencePiece tokenizer -- Word embeddings -- FastText
Summary Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book Description Although creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you'll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you'll address the most challenging aspect of large-scale machine learning systems - ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began - large-scale machine learning software. What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you're a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product
Notes Description based upon print version of record
Bibliography Includes bibliographical references and index
Notes From feature extraction to models
Subject Machine learning.
Software engineering.
Form Electronic book
ISBN 9781837636945
183763694X