Training Machine & Deep Learning models are essential for AI projects. 

At Babel, we have our own methodology used in Machine Learning training processes from structured data in order to extract useful knowledge from them, as well as the use of the main AI algorithms and services. 

The procedures to be developed to carry out this knowledge extraction will depend largely on the nature of the problems posed in the customer model to be trained, as well as on the structured data available. 

The first step is to define the target problem: the one posed by the customer. The first step we focus on the detailed definition of the problem to be solved with the customer using agile techniques, as well as its categorisation in the taxonomy of this type of problems. Both the type of data and the problem will fall into one of these categories. 

The key to successful model training projects lies in learning and training with historical data to make inferences on the present data and to estimate future predictions by applying different techniques. 

In an exploratory analysis, a descriptive statistical analysis of the data is required in order to understand the data and to find some clear indications of causal relationships between the explanatory variables and the target variable, either from the measured characteristics or from their evolution over time. In this first phase, the nature of the variables and the quality of the data will be studied, and a first selection of variables and data of interest will be made. 

Modelling: Training and validation

The choice of model is a key element of the training process under Machine Learning. At Babel, we analyse the possible statistical models, algorithms, methodologies and strategies with our team of doctors.

Model training is an iterative process in which we try to optimise and improve an objective function and score by adjusting its parameters in order to provide the expected result on the basis of a training set.