During the Drifting evaluation, I'll check two types of drift.
Considering tabular data and only numeric features I'll propose a statistical hypothesis system approach to reveal distributional changes over time. It's likely that the multivariate distribution doesn't follow a standard form, so, I'll create a hypothesis system based on permutations and distances. The idea is to cover sudden, seasonal, and gradual changes.
For more complex solutions we can think to use VAE and its latent adjusted distribution to check distributional changes.
In the same vein. I'll create a pipeline that checks model performance (e.g., accuracy) and degradement during the time (e.g., chart control) that prevents the model to decay below established limits. It's important to remind the existence of curated data for this aim.
During the Drifting evaluation, I'll check two types of drift.
Considering tabular data and only numeric features I'll propose a statistical hypothesis system approach to reveal distributional changes over time. It's likely that the multivariate distribution doesn't follow a standard form, so, I'll create a hypothesis system based on permutations and distances. The idea is to cover sudden, seasonal, and gradual changes.
For more complex solutions we can think to use VAE and its latent adjusted distribution to check distributional changes.
In the same vein. I'll create a pipeline that checks model performance (e.g., accuracy) and degradement during the time (e.g., chart control) that prevents the model to decay below established limits. It's important to remind the existence of curated data for this aim.