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Engineering discipline for building, evaluating, and reliably operating AI-based systems.
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AI engineering combines software development, data work, and machine learning to build and operate robust AI systems for real-world tasks.
AI engineering emerged as a distinct field from combining software engineering, systems, computer science, and human-centered design. Carnegie Mellon University's Software Engineering Institute's AI Engineering Body of Knowledge consolidates this development for repeatable implementation of AI systems.
An AI engineering team treats an AI system as a product with a data pipeline, model, application, operations, and safeguards. These parts are tested, versioned, monitored, and improved together based on actual utility.
The model, data, application, and operations are designed as one system.
Training, delivery, monitoring, and evolution belong together.
The technical solution is aligned with human needs and goals.
AI engineering helps turn prototypes into dependable AI products and keep their quality measurable in operation.
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