CAFA Tech AI Training Centre

AI Training Input module: This module involves collecting and importing data from various sources into the system for data processing and analysis. It can include mechanisms for batch and real-time data processing. After data is ingested, it’s stored in databases. This storage can be on-premises or in the cloud, depending on the architecture.

AI Analyses Input Module: Data often needs to be cleaned, normalized, and transformed into a format suitable for training AI models. This step can involve handling missing data, encoding categorical variables, and scaling or normalizing numerical data.

AI Core. Model Training is the core of an AI Training Centre, where machine learning algorithms learn from the data. This can involve selecting algorithms, tuning hyperparameters, and using techniques like cross-validation.

Model Evaluation: After a model is trained, it’s evaluated to determine its performance. Metrics such as accuracy, precision, recall, and F1 score are commonly used for classification tasks, while mean squared error or mean absolute error might be used for regression tasks.

Object Classification Output Module (Intelligence Decision Support). Model Deployment: Once a model is trained and evaluated, it’s deployed into a working environment. This can involve setting up APIs for real-time predictions or batch processing systems.

Monitoring and Maintenance. After deployment, models need to be monitored for performance decay over time. Retraining pipelines may be set up to periodically update the model with new data.