The structured set of methodologies, workflows, and best practices governing how software — including AI and machine learning systems — is conceived, built, tested, deployed, and maintained. For AI systems this encompasses data-centric workflows, experiment tracking, model validation protocols, MLOps pipelines, and cross-functional collaboration between data scientists, engineers, and domain experts across the full model lifecycle.
Semantic Classification
Content
Key Characteristics
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Integrates data science and software engineering practices
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Employs version control for data, code, and models
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Implements automated testing and validation pipelines
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Facilitates collaboration across multidisciplinary teams
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Incorporates continuous integration and deployment (CI/CD)
Overview
Software Development Process for AI encompasses methodologies, workflows, and best practices for building, testing, deploying, and maintaining machine learning systems. This includes agile AI development, data-centric workflows, experiment tracking, model validation protocols, and MLOps practices. Key activities span problem formulation, data collection and labeling, exploratory data analysis, model prototyping, hyperparameter tuning, production deployment, monitoring, and continuous improvement. Modern processes emphasize cross-functional collaboration, reproducibility, version control (code, data, models), and ethical considerations.
Related Concepts
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References
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Amershi, S. et al. (2019). Software Engineering for Machine Learning: A Case Study. ICSE-SEIP 2019.
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Kim, M. et al. (2022). The MLOps Lifecycle: A Survey of Challenges, Solutions and Future Directions. arXiv:2203.02302.
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Paleyes, A. et al. (2022). Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Computing Surveys, 55(6), 1-29.