Summary
In this chapter, you’ve explored a comprehensive framework for operationalizing GenAI integration patterns. You’ve learned about a four-layer approach that addresses the complexities of deploying and maintaining production-grade GenAI applications, encompassing the Data, Training, Inference, and Operations layers.
We proposed a holistic strategy that emphasizes the importance of data quality, security, and governance in the Data layer, while also addressing regulatory compliance and ethical considerations. The Training layer introduced you to various model adaptation techniques, including few-shot learning, fine-tuning, and full training, along with crucial aspects of model governance, performance monitoring, and XAI.
You’ve learned that the Inference layer focuses on scalability, performance optimization, and secure deployment strategies, including edge and distributed inference capabilities. The section on the Operations layer highlighted the...
