MLOps Automation — CI/CD/CT for Machine Learning (ML) Pipelines
Scaling the use of AI/ML by building Continuous Integration (CI) / Continuous Delivery (CD) / Continuous Training (CT) pipelines for ML based applications
Background
In my previous article:
MLOps in Practice — De-constructing an ML Solution Architecture into 10 components
I talked about the importance of building CI/CT/CD solutions to automate the ML pipelines. The aim of MLOps automation is to continuously test and integrate code changes, continuously train new models with new data, upgrade model performance when required, and continuously serve and deploy models to a production environment in a safe, agile and automated way.
In today’s article, we are going to dive into the topic of MLOps automation. Specifically we are going to cover the following components:
- Why is MLOps automation necessary?
- A high-level introduction to DevOps and CI/CD and its relevance to ML
- What’s special about MLOps compared to DevOps?
- A sample CI/CD architecture for ML based systems
Now let’s get started by understanding why MLOps automation is necessary.
