Perspective On Quality, Experiment And Risk

Product quality, experimentation, and risk are key elements that organizations evaluate when they begin Machine Learning. Innovators show interest and willingness to take risks, but ML leaders focus on quality and reproducibility. For example, 67% of innovators indicate that experimentation and risk are promoted, even if this causes failures to deploy models, but only 6% of ML leaders show that experimentation and risk are encouraged.

ML performers seek a balance between retaining quality and reproducibility and taking risks while experimenting with models. However, 29% of performers show that quality and reproducibility are priorities, and 28% show interest and willingness to take the risk

An important data point we found is that 29% of performers show an even mix of taking risks and retaining quality and reproducibility. In contrast, Observers show the same trend as performers, but with less proportion of taking risks and maintaining quality and reproducibility.
This is just a snippet from our Q2 2021 MLR Report.