Exploring the model development components
Once the cleaned data is available, data scientists then go through the problem and try to determine what set of patterns would be helpful for the situation. The key here is that the data scientist's primary role is to find patterns in the data. Model development components of the ML platform explore data patterns, build and train ML models, and trial multiple configurations to find the best set of configurations and algorithms to achieve the desired performance of the model.
Within the course of model development, data scientists or ML engineers build multiple models based on multiple algorithms. These models are then trained using the data gathered and prepared from the data engineering flow. The data scientist then plays around with several hyperparameters to get different results from model testing. The result of such training and testing is then compared with each of the other models. These experimentation processes are then repeated...
