We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace.

Apr 8, 2025 · Below are the basic components and their notations of a State Machine Diagram: 1. Initial state. We use a black filled circle represent the initial state of a System or a Class. 2. Transition. We use a solid arrow to represent the transition or …

In this context, we investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective.

In 2024, the Software Engineering Institute (SEI) released Machine Learning Test and Evaluation (MLTE, referred to as “melt”), a process and tool co-developed by the SEI and the Army AI Integration Center (AI2C) to evaluate ML models at each step in the development process and increase ML model production readiness.

Dec 14, 2017 · Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering...

Dec 18, 2024 · Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation.

Jun 17, 2024 · A machine-learning algorithm (sometimes also called modeling technique), implemented in a machine-learning library or machine-learning framework, such as sklearn or Tensorflow, defines the training procedure of how the function is learned from the observations.

Software engineering is an analytical study and process that is systematic, well-organized, and utilized to develop, operate, and maintain software systems. How can Machine Learning be used in Software Engineering? The fundamental principles of …

Feb 15, 2021 · In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.

By categorizing the rationales behind the selection of ML/DL techniques into five themes, we analyzed how model performance, robustness, interpretability, complexity, and data simplicity affected the choices of ML/DL models.