AI System Engineering Task

A AI System Engineering Task is a software engineering task that produces engineered AI systems.

• Context:
  • It can (typically) be performed by an AI Engineer with engineering skills.
  • It can (typically) require AI Engineering Practices for system development.
  • It can (often) involve AI System Design and model development.
  • It can (often) follow AI Best Practices for production deployment.
  • ...
  • It can range from being a Simple AI Engineering Task to being a Complex AI Engineering Task, depending on its system scope.
  • It can range from being a Experimental AI Task to being a Production AI Task, depending on its deployment stage.
  • It can range from being a Specialized AI Task to being a General AI Task, depending on its application domain.
  • ...
  • It can include AI System Evaluation using performance measures.
  • It can require AI System Design for system architecture.
  • It can involve AI System Development using machine learning techniques.
  • It can perform AI System Deployment into production systems.
  • It can support AI System Monitoring and model maintenance.
  • It can implement AI Infrastructure Engineering for large-scale deployments.
  • It can address AI Ethics and fairness considerations.
  • It can require Data Scientist collaboration for model implementation.
  • It can use AI Platforms during the development lifecycle.
  • It can follow Engineering Best Practices for system quality.
  • ...
• Example(s):
  • Machine Learning Engineering Tasks, which develop ML systems, such as:
    • Neural Network Engineering
    • Deep Learning System Development
    • Model Training Tasks
    • ...
  • Applied AI Engineering Tasks, which create domain-specific systems, such as:
    • Computer Vision System development
    • Natural Language Processing System implementation
    • Recommendation System deployment
    • ...
  • AI Infrastructure Tasks, which support AI operations, such as:
    • Model Serving Tasks
    • AI Pipeline Engineering
    • System Integration Tasks
    • ...
• Counter-Example(s):
  • Data Engineering Tasks, which focus on data pipelines rather than AI systems.
  • Rule-Based System Tasks, which use static rules rather than machine learning.
  • Research AI Tasks, which explore theoretical approaches rather than engineering solutions.
• See: Machine Learning Engineering, AI Model Development, Data Science Task.

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