Case Study: Predict Loan Approval Decisions Using AI

AI solution life cycle

Have you wondered what goes on behind the scenes when we receive decisions from the AI model?

The AI solution life cycle encompasses the entire process, from data preparation to model management. Here’s a brief overview of each stage:

• Problem formation and data source identification: This initial stage involves clearly defining the problem or objective that the AI solution aims to address. It includes understanding the business or domain requirements, identifying the specific problem statement, and determining the relevant data sources for the project. During this stage, stakeholders collaborate to establish the scope, goals, and success criteria for the AI solution.

• Data preparation: This stage involves gathering, cleaning, and transforming the raw data for use in the AI solution. It includes tasks such as data collection, data cleaning, data integration, and feature engineering, where relevant features are extracted or created to enhance the model’s performance.

• Model building: In this stage, the AI model architecture is defined and developed based on the problem at hand. It involves selecting appropriate algorithms, designing the model structure, and setting hyperparameters. The model-building stage focuses on creating a functional model that can process input data and produce the desired output predictions.

• Model training and tuning: Once the model is built, it needs to be trained using labeled data. This involves feeding the model with input data and corresponding target labels, allowing it to learn and adjust its internal parameters to minimize errors and improve accuracy. Model tuning is performed to optimize the model’s performance by ...