Scalable Deep Learning Pipelines with Apache Airflow

In the preceding chapters, we built scalable deep learning pipelines and executed them manually on AWS EC2 instance. While this non-automated approach is common in the industry, it may not always be the most efficient. On the one hand, it lacks visibility into the status and progress of workflows, necessitating separate implementation of monitoring and logging. On the other hand, it can also result in challenges when modifying or extending the workflow. Indeed, scaling workflows to manage large volumes of data or computational resources can be daunting without a dedicated orchestration framework. Additionally, ensuring reproducibility of experiments becomes challenging without a standardized approach, relying heavily on version control and documentation.