Securing and Automating Machine Learning with Airflow

Problem

Emmi had an exceptional team of data scientists but no infrastructure or backend engineering support. Their larger customers were demanding a security baseline — production data needed to be processed in a secure, auditable cloud environment. The data science team was spending time on infrastructure problems instead of their core ML work.

Action

After consulting with domain experts and evaluating data pipeline platforms, I chose Apache Airflow via AWS MWAA (Managed Workflows for Apache Airflow). This gave us a managed, secure orchestration layer that the data scientists could build on directly. The entire infrastructure was codified with Terraform for reproducibility and auditability.

Outcome

Emmi had a secure production data pipeline running in AWS, fully codified in Terraform and managed through MWAA. The data science team went back to focusing on their core ML work, and the company met its customers’ security requirements — unblocking enterprise deals that had been stalled on compliance.