data from the trenches

Combining ML and drones, a data scientist dream. We’ll show how to deploy a SSD model on a drone with Uavia DroneOS and Dataiku DSS.

Your data has drifted, and you now need to determine the source of the drift. This can be achieved leveraging interpretability techniques.

Augmenting any model with an uncertainty estimation? It might sound too good to be true, but that’s the promise of conformal prediction.

We give an in-depth overview of the various definition of fairness for Machine Learning. Spoiler alert: we can’t have them all.

Deep learning models are known for their impressive size, but how do you deploy those on edge devices where size matters?

Detecting drift is critical in the monitoring of deployed models. So, how do we efficiently detect data drift ?

We review and experiment the various categorical encoding strategies of xgboost, lightgbm and catboost.

We present the inner workings of XGBoost, Lightgbm, HistGradBoosting and compare their performance. Does one stand out ?

Spoiler alert: on average, all algorithms except grid search produce similarly performant models.

When Machine Learning models are not relevant anymore, it might be due to underlying data drift. Here, we introduce and review data drift.

Studying Hyperparameter Importance to Speed Up Optimization

Rounding operations can lead Hyperband not to use 7% of the available budget. We propose a method that reduces unused budget to 3%.