Miroslaw Staron – författare

Engineering Generative-AI Based Software discusses both the process of developing this kind of AI-based software and its architectures, combining theory with practice. Sections review the most relevant models and technologies, detail software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, explore various architectural styles and tactics for such systems, including different programming platforms, and show how to create robust licensing models. Finally, readers learn how to manage data, both during training and when generating new data, and how to use generated data and user feedback to constantly evolve generative AI-based software.As generative AI software is gaining popularity thanks to such models as GPT-4 or Llama, this is a welcomed resource on the topics explored. With these systems becoming increasingly important, Software Engineering Professionals will need to know how to overcome challenges in incorporating GAI into the products and programs they develop.Provides a holistic software engineering approach to constructing generative AI softwareShows how to use modern models to create long-term products that bring valuePresents a combination of process, product, and business models for generative AI-based software engineering

Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software productsKey FeaturesLearn 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 DescriptionAlthough 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 learnIdentify 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 forIf 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.

They wrote this book to help readers define, implement, deploy and maintain company-wide measurement programs, which consist of a set of measures, indicators and roles that are built around the concept of measurement systems.

This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations.

This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations.

This book introduces the concept of software architecture as one of the cornerstones of software in modern cars.

This book introduces the concept of software architecture as one of the cornerstones of software in modern cars.

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.

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.

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.

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.

This book introduces the concept of software architecture as one of the cornerstones of software in modern cars.