# **Lifecycle Management of AI Agents** # **Definition/Description** AI lifecycle management involves the stages of development, from data curation and training to evaluation, deployment, and continuous improvement. # **Key Points** - **Stages**: Data curation, model training, fine-tuning, synthetic data generation, evaluation, and deployment. - Uses libraries like Nvidia Nemo for seamless integration. - Continuous improvement ensures agents remain relevant and effective. # **Insights** Lifecycle management emphasizes scalability and adaptability, ensuring AI agents can evolve with business needs. This structured approach also underscores the importance of data and training quality. # **Connections** - Related Notes: [[AI Ecosystems and Infrastructure]], [[Nvidia’s Role in AI Development]] - Broader Topics: [[Software Development Processes]], [[Artificial Intelligence Frameworks]] # **Questions/Reflections** - What challenges exist in maintaining AI systems post-deployment? - How can lifecycle management frameworks be standardized across industries? # **References** - Nvidia AI libraries and tools like Nemo. - Best practices for AI lifecycle management.