Overview
AI System Lifecycle Management is crucial for ensuring that AI technologies are developed, deployed, and maintained effectively. By understanding the various stages of the lifecycle, from development to ethical considerations, stakeholders can ensure that AI systems are not only functional but also ...
Key Terms
Example: The AI lifecycle includes development, deployment, and maintenance.
Example: Model training involves feeding data to the algorithm to learn patterns.
Example: Deployment can involve launching a chatbot on a website.
Example: Monitoring helps identify when an AI model needs retraining.
Example: Bias can lead to unfair treatment of certain groups in AI applications.
Example: Ethics in AI includes ensuring fairness and transparency.
Related Topics
Key Concepts
DevelopmentDeploymentMonitoringMaintenance