Christian Kästner·Feb 11, 2024Model QualityBeyond model accuracy and its pitfalls, we discuss emerging behavioral evaluation strategies inspired by traditional software testing
Christian Kästner·Dec 20, 2022Security 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…A response icon1A response icon1
Christian Kästner·Nov 17, 2022Integration and System TestingAfter testing models, pipelines, and other components, we finally that the integration and the system as a whole work…
Christian Kästner·Nov 14, 2022Quality Assurance for Machine-Learning PipelinesBeyond model quality and data quality, the quality of the pipeline matters. We discuss testability and testing, code review, static…
Christian Kästner·Nov 3, 2022Quality Assurance BasicsThere are many strategies to evaluate software with and without machine learning, not just testing. This chapter provides an overview.
Christian Kästner·Oct 27, 2022Safety in ML-Enabled SystemsMachine learning tends to complicate safety considerations since machine-learned models always may make mistakes. Hazard analysis helps…
Christian Kästner·Sep 13, 2022Planning for Machine-Learning MistakesMachine-learned models will always make mistakes, but with some planning we can anticipate and mitigate many by designing for failures…
Christian Kästner·Sep 12, 2022Gathering Requirements for ML -Enabled SystemsMany developers building products with ML components will benefit from taking requirements engineering more seriously, checking…
Christian Kästner·Sep 2, 2022Setting 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…
Christian Kästner·Sep 1, 2022When 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…
