What data preparation is required before AI deployment?

Data preparation involves collecting, cleansing, and transforming raw data into a suitable format for training and deploying AI models; it is a mandatory step before deployment to ensure model functionality and accuracy.

Key tasks include identifying and sourcing relevant data, rigorously cleaning it to handle missing values, duplicates, and outliers, labeling data for supervised learning models, and splitting it into distinct training, validation, and test sets. The data must be representative of real-world scenarios the model will encounter, sufficient in volume and quality, and have consistent feature definitions.

Adequate data preparation directly enables effective model training, significantly improves prediction accuracy, and enhances model robustness in production. It addresses data drift risks and prevents technical failures upon launch, thereby reducing deployment delays and ensuring the AI solution delivers reliable business value from the outset. Thorough preparation ultimately underpins the success and trustworthiness of the deployed AI system.