MLOps is Data Engineering

Why MLOps is Data Engineering

MLOps is the practice of applying DevOps principles to the development and deployment of machine learning models.

It extends the DevOps framework to address the unique engineering challenges associated with machine learning. This includes

• Managing Platform using Cloud Infrastrure (IaC)
• Automating data pipelines to ensure efficient data flow and processing (Engineering)
• Automating ml-model pipelines to build, deploy and serve machine learning models.(Engineering)
• Continuous Integration and Continuous Deployment (CI/CD) pipelines,
• and providing ongoing support and maintenance through DataOps practices.

By integrating these components, MLOps aims to streamline the entire machine learning lifecycle, from model development to production, ensuring scalability, reliability, and efficiency.

Let’s break down the different components, magnify them, and explain exactly how engineering principles all fit into MLOps framework.

Feature Stores — The engineering problems here are more related to data engineering.

Feature stores involve building data pipelines to extract, transform, and prepare data for machine learning model consumption. This process mirrors a data engineering pipeline, where data is transformed into a star schema with dimensions and measures, making it accessible…