MLOps
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning (ML), software engineering, and DevOps to automate and standardize the lifecycle of AI models. It bridges the gap between model experimentation and production, allowing teams to build, deploy, monitor, and maintain models efficiently and reliably.
Why is MLOps needed?
Unlike traditional software, Machine Learning (ML) systems depend on both code and data. An ML model in production can lose accuracy over time (a phenomenon known as "model drift") as real-world data changes. MLOps solves the following challenges:
- Inefficient Workflows: Automates the testing, retraining, and deployment phases so data scientists aren't stuck doing manual IT work.
- Model Drift and Decay: Continuously monitors live models so they can be automatically retrained when their accuracy drops.
- Reproducibility: Ensures that every dataset, code change, and model parameter is version-controlled so results can be audited and reproduced.
The Core MLOps Lifecycle
The standard MLOps pipeline connects the work of data scientists, data engineers, and IT operations through three main stages:
- Design: Understanding business requirements and defining exactly what data is needed.
- Build: Developing the machine learning model, which includes data preparation, feature engineering, and model training.
- Operate: Packaging the model, deploying it into a live environment (via CI/CD pipelines), and continuously monitoring it for performance and compliance
