Data Modeling – Machine Learning Fundamental Concepts

Data Modeling

It is the process of modeling the data flow. In machine learning, a model represents a decision process in an abstract manner. It is the process of designing how the data will flow in and out of the databases during the system’s operation. This part, which is both very interesting and necessary for any machine learning application, can be broken down into two stages:

• Develop and train the model: On the basis of the training dataset, multiple models are constructed with the assistance of various algorithms.
• Create and conduct model training: After the training stage, the models are validated and fine-tuned with the help of the validation dataset. Additionally, the test dataset is utilized in the process of evaluating and selecting the single most effective model.

Deployment

The process of moving a machine learning model from an offline environment and integrating it into a live space with a real production environment is known as “deployment” in machine learning, for instance, in an application that is live. Being a critical step, it needs to be dealt with carefully because the working of a model depends upon its complete and appropriate deployment. Luckily, we do have a highly accurate model with which to address this problem.

This component can be further divided into two stages:

• Model deployment: Once a working model is testified and approved by the supervising team, the deployment team takes it upon themselves to deploy the model in a live environment. This phase usually consists of several steps within itself, and they are as follows:

Moving the model into an environment that includes servers and middleware. At this
1) stage, our model gets access to all the hardware and data facilities it requires.
2) The model is then integrated into a process using APIs or incorporated into the user’s software connecting to the end user’s computer.
3) And in the final stage, the users access the model, run it, and access its outputs.

• Monitor/update the model and data: As more time passes, there is a possibility that the patterns in the data will undergo abrupt or seasonal shifts, which may result in a decline in the performance of the model. In these kinds of situations, the model needs to be updated, which means going back to the phases of retrieving data, which ultimately brings the workflow full circle.