When data preparation is ready, we move to model development, where we train the model and evaluate the results.

This involves two steps:

1. Model training
2. Model evaluation

This is an iterative process.

In Vertex AI, we need to specify:

• training method (dataset)
• training objective: the goal of the model training and the task you want to solve
• training method: AutoML or custom training
• training details
• budget and pricing

Vertex AI provides extensive evaluation metrics to help determine a model’s performance.

Among these metrics we have Precision and Recall from a confusion matrix:

• Precision is the fraction of predicted positives that are actually positive: $\frac{TP}{TP + FP}$
• Recall is the fraction of actual positives that were correctly labelled: $\frac{TP}{TP + FN}$

where TP is True Positives, FP is False Positives and FN is False Negatives

Precision and recall are often a trade-off.

In addition to the confusion matrix and the metrics generated to measure recall and precision, the other useful measurement is feature importance. In Vertex AI, feature importance is displayed through a bar chart to illustrate how each feature contributes to a prediction.

This information helps decide which features are included in a machine learning model to predict the goal.

Explanable AI is a set of tools and frameworks to help understand and interpret predictions made by machine learning models in Vertex AI