Miroslaw Staron – författare
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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 levelSecure the quality of your machine learning pipeline at runtimeApply your knowledge to natural languages, programming languages, and imagesBook 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 needsWork with scalable machine learning pipelinesScale up pipelines from prototypes to fully fledged softwareChoose suitable data sources and processing methods for your productDifferentiate raw data from complex processing, noting their advantagesTrack and mitigate important ethical risks in machine learning softwareWork with testing and validation for machine learning systemsWho 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.
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843 kr
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This book introduces the concept of software architecture as one of the cornerstones of software in modern cars. Following a historical overview of the evolution of software in modern cars and a discussion of the main challenges driving that evolution, Chapter 2 describes the main architectural styles of automotive software and their use in cars’ software. Chapter 3 details this further by presenting two modern architectural styles, i.e. centralized and federated software architectures. In Chapter 4, readers will find a description of the software development processes used to develop software on the car manufacturers’ side. Chapter 5 then introduces AUTOSAR – an important standard in automotive software. Chapter 6 goes beyond simple architecture and describes the detailed design process for automotive software using Simulink, helping readers to understand how detailed design links to high-level design. The new chapter 7 reports on how machine learning is exploited in automotive software e.g. for image recognition and how both on-board and off-board learning are applied. Next, Chapter 8 presents a method for assessing the quality of the architecture – ATAM (Architecture Trade-off Analysis Method) – and provides a sample assessment, while Chapter 9 presents an alternative way of assessing the architecture, namely by using quantitative measures and indicators. Subsequently Chapter 10 dives deeper into one of the specific properties discussed in Chapter 8 – safety – and details an important standard in that area, the ISO/IEC 26262 norm. Lastly, Chapter 11 presents a set of future trends that are currently emerging and have the potential to shape automotive software engineering in the coming years.
This book explores the concept of software architecture for modern cars and is intended for both beginning and advanced software designers. It mainly aims at two different groups of audience – professionals working with automotive software who need to understandconcepts related to automotive architectures, and students of software engineering or related fields who need to understand the specifics of automotive software to be able to construct cars or their components. Accordingly, the book also contains a wealth of real-world examples illustrating the concepts discussed and requires no prior background in the automotive domain. Compared to the first edition, besides the two new chapters 3 and 7 there are considerable updates in chapters 5 and 8 especially.656 kr
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This book celebrates the 10-year anniversary of Software Center (a collaboration between 18 European companies and five Swedish universities) by presenting some of the most impactful and relevant journal or conference papers that researchers in the center have published over the last decade.
The book is organized around the five themes around which research in Software Center is organized, i.e. Continuous Delivery, Continuous Architecture, Metrics, Customer Data and Ecosystems Driven Development, and AI Engineering. The focus of the Continuous Delivery theme is to help companies to continuously build high quality products with the right degree of automation. The Continuous Architecture theme addresses challenges that arise when balancing the need for architectural quality and more agile ways of working with shorter development cycles. The Metrics theme studies and provides insight to understand, monitor and improve software processes, products and organizations. The fourth theme, Customer Data and Ecosystem Driven Development, helps companies make sense of the vast amounts of data that are continuously collected from products in the field. Eventually, the theme of AI Engineering addresses the challenge that many companies struggle with in terms of deploying machine- and deep-learning models in industrial contexts with production quality. Each theme has its own part in the book and each part has an introduction chapter and then a carefully selected reprint of the most important papers from that theme.
This book mainly aims at researchers and advanced professionals in the areas of software engineering who would like to get an overview about the achievement made in various topics relevant for industrial large-scale software development and management – and to see how research benefits from a close cooperation between industry and academia.1 678 kr
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Software, System, and Service Engineering
S3E 2023 Topical Area, 24th Conference on Practical Aspects of and Solutions for Software Engineering, KKIO 2023, and 8th Workshop on Advances in Programming Languages, WAPL 2023, Held as Part of FedCSIS 2023, Warsaw, Poland, 17–20 September 2023, Revised Selected Papers
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This book constitutes the refereed proceedings of the S3E 2023 Topical Area, 24th Conference on Practical Aspects of and Solutions for Software Engineering, KKIO 2023, and 8th Workshop on Advances in Programming Languages, WAPL 2023, as Part of FedCSIS 2023, held in Warsaw, Poland, during September 17–20, 2023.
The 6 revised papers presented in this book were carefully reviewed and selected from a total of 55 submissions. They focus on new ideas and developments in practical aspects and solutions for software engineering.Software, System, and Service Engineering
S3E 2024 Topical Area, Held as Part of FedCSIS 2024, Belgrade, Serbia, 8–11 September, 2024 and KKIO 2024, Held as Part of SEAA 2024, Paris, France, August 28-30, 2024, Revised Selected Papers
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This book constitutes selected and enlarged versions of papers presented at S3E 2024 Topical Area, held as part of FedCSIS 2024, in Belgrade, Serbia, 8–11 September, 2024 and the 25th Conference on Practical Aspects of and Solutions for Software Engineering, KKIO 2024, held as part of SEAA 2024, Paris, France, during August 28-30, 2024.
The 3 papers included from KKIO 2024 were selected from 18 submissions (and 10 presentations), and the 5 papers from S3E were selected from 25 submissions (and 12 presentations). The contributions deal with academic research and real-world applications in the field of software engineering.
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