Paper ID: 2205.11473

Rethinking Streaming Machine Learning Evaluation

Shreya Shankar, Bernease Herman, Aditya G. Parameswaran

While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are performing unexpectedly. In this position paper, we discuss how the nature of streaming ML problems introduces new real-world challenges (e.g., delayed arrival of labels) and recommend additional metrics to assess streaming ML performance.

Submitted: May 23, 2022