Summary
This chapter outlined the construction and execution of an automated pipeline for the T5-11b LLM using Apache Airflow, detailing steps from data ingestion and feature creation to fine-tuning, hyperparameter tuning, and storage. It encapsulated the essential phases of model development, leveraging tools such as Feast for feature storage and management, and orchestrated these tasks to streamline the process of bringing a sophisticated LLM to production readiness.
Looking ahead to the next chapter, we’ll examine the steps required to implement robust model governance and review protocols to ensure the model’s ethical use, reliability, and ongoing performance in real-world applications, addressing critical aspects such as model bias, fairness, and regulatory compliance.
