Automating model development

Integrating the stages of machine learning model development into an automated pipeline demands a sophisticated orchestration of tools and technologies. This integration aims to automate the workflow from feature creation to model fine-tuning and hyperparameter optimization, ensuring efficiency and scalability. Incorporating scheduling, monitoring, alerting, and checkpointing into this pipeline enhances its robustness and reliability. Let’s examine how each stage can be integrated into an automated pipeline using modern tools and practices.

Apache Airflow allows us to define tasks and dependencies in a Directed Acyclic Graph (DAG). Here’s an example of an Airflow DAG for our LLM pipeline. The example assumes that we import all the scripts written in this chapter for each step of the DAG:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.operators.dummy_operator import DummyOperator
from...