Six Stages of Data-Centric MLOps

What are the steps to ensure data quality in MLOps?

· 1. Scoping
· 2. Collecting
∘ Privacy-protected
∘ Trustworthy
∘ Balance
∘ Diversity
· 3. Labeling
· 4. Training
· 5. Deploying
· 6. Monitoring
· Conclusion
· Reference

Photo by MJ Tangonan on Unsplash

In the previous article, I argued that MLOps needs to operate around data given the historical development of AI. The details on how to manage data-centric MLOps are the focus of the current article.

Servicing an AI system in production requires an engineering approach. What that means is that the operations need to be systematic and repeatable with the necessary tools and processes.

A typical ML pipeline goes through six stages:

You will see that concerns for data need to be at every stage.

1. Scoping

At the Scoping stage, big questions need to be answered such as:

• What problems do we need to solve?
• Do we need AI, Machine Learning, or Deep Learning solutions?