Adding experiments
So, in this section, we will use the experiments module in MLflow to track the different runs of different models and post them in our workbench database so that the performance results can be compared side by side.
The experiments can actually be done by different model developers as long as they are all pointing to a shared MLflow infrastructure.
To create our first, we will pick a set of model families and evaluate our problem on each of the cases. In broader terms, the major families for classification can be tree-based models, linear models, and neural networks. By looking at the metric that performs better on each of the cases, we can then direct tuning to the best model and use it as our initial model in production.
Our choice for this section includes the following:
- Logistic Classifier: Part of the family of linear-based models and a commonly used baseline.
- Xgboost: This belongs to the family of tree boosting algorithms where many weak...
