Are You Ready to Engineer and Sustain AI Systems?

Abstract

Contents include: AI at CMU and AI at the SEI; AI-enabled systems are software systems!; Can we Design and Analyze AI-Enabled Systems Predictably?; Architecture Challenge #1: Lack of Systems Perspective; Recommendation: Manage AI Component, Data, and Architectural Dependencies; Architecture Challenge #2 : Inability to separate data and system attributes; Recommendation: Understand High-Priority Quality Attributes of ML-Enabled Systems; Architecture Allows Improving Predictability of Data and Other System Component Interactions; Architecture Challenge #3 : Lack of Monitorability; Recommendation: Decouple Different Aspects of Monitorability; Recommendation: Integrate the analyses performed by the Data Scientist into the MLOps pipeline; Architecture Challenge #4 : Different Paces of Change; Recommendation: Embrace Changing Anything Changes Everything Principle*.

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Document Details

Document Type
Technical Report
Publication Date
Nov 11, 2022
Accession Number
AD1185241

Entities

People

  • Ipek Ozkaya

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computer Science
  • Data Sets
  • Department Of Defense
  • Engineering
  • Information Processing
  • Information Systems
  • Learning
  • Machine Learning
  • Materials
  • Monitoring
  • Software Design
  • Software Development
  • Technical Debt
  • Training
  • Universities

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy