Challenge
Data engineering and machine learning pipelines are both very different but oddly can feel very similar. Many ML engineers I have talked to in the past rely on tools like Airflow to deploy their batch models.
Data Engineering Pipelines
First, let’s dive into data pipelines. Data pipelines are a series of processes that extract data from different sources, clean it, and then store it in a data warehouse or database. This is important for companies because it helps them make informed decisions. A data pipeline is made up of four parts:
Machine Learning Pipelines
Machine Learning (ML) pipelines don’t work in a straight line like data pipelines. Instead, they involve building, training, and deploying ML models. This is used to automate the entire process of building an ML model, from collecting data to deploying it in production. An ML pipeline is made up of five parts: