What is an AI (Artificial Intelligence) Project?



Define the Project's Objective

The first step is to understand the project's needs. Clearly defining the goal and the questions to be answered will help us define the key metrics that will be used to evaluate the project.


Collect Data

Data collection is a key element in any Data Science project. This includes identifying relevant data sources, and collecting and modifying them for subsequent use.


Data Preprocessing

Data is often raw and disorganized, and therefore preprocessing is a vital step in achieving accurate results. This includes cleaning, treating, and even normalizing them.


Model Selection

At this step, the model that best fits the project's needs and available data must be selected. This also includes the selection of algorithms, model validation, and parameter tuning.


Training and Evaluation of the Model

At this point, the selected model must be trained and its performance evaluated using the metrics defined in the first step. Cross-validation and data splitting are some of the techniques used.


Model Deployment

Finally, the model is deployed in a real-time production environment. This includes integrating the model with client applications and establishing automated workflows for data collection, preprocessing, and updating.

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