Model QualityBeyond model accuracy and its pitfalls, we discuss emerging behavioral evaluation strategies inspired by traditional software testingFeb 11Feb 11
Security and Privacy in ML-Enabled SystemsA brief overview of common security and privacy concerns, new challenges introduced with machine-learning components, and common design s…Dec 20, 20221Dec 20, 20221
Integration and System TestingAfter testing models, pipelines, and other components, we finally that the integration and the system as a whole work…Nov 17, 2022Nov 17, 2022
Quality Assurance for Machine-Learning PipelinesBeyond model quality and data quality, the quality of the pipeline matters. We discuss testability and testing, code review, static…Nov 14, 2022Nov 14, 2022
Quality Assurance BasicsThere are many strategies to evaluate software with and without machine learning, not just testing. This chapter provides an overview.Nov 3, 2022Nov 3, 2022
Safety in ML-Enabled SystemsMachine learning tends to complicate safety considerations since machine-learned models always may make mistakes. Hazard analysis helps…Oct 27, 2022Oct 27, 2022
Planning for Machine-Learning MistakesMachine-learned models will always make mistakes, but with some planning we can anticipate and mitigate many by designing for failures…Sep 13, 2022Sep 13, 2022
Gathering Requirements for ML -Enabled SystemsMany developers building products with ML components will benefit from taking requirements engineering more seriously, checking…Sep 12, 2022Sep 12, 2022
Setting and Measuring Goals for Machine Learning ProjectsTo build products successfully, it is important to understand both the goals of the entire system and the goals of the model within…Sep 2, 2022Sep 2, 2022
When to use Machine LearningMachine learning is often pursued because of hype rather than to solve an actual goal. When is it really appropriate? This chapter…Sep 1, 2022Sep 1, 2022
