Building an Enterprise-Grade Real-Time MLOps Platform: A Complete Practice from Automated Training to Continuous Deployment

Explore the design and implementation of a production-grade MLOps platform, covering real-time prediction, automated retraining, data drift detection, CI/CD pipelines, and cloud-native deployment, providing a reference architecture for the engineering implementation of machine learning systems.

• Original Author/Maintainer: rajaka43
• Source Platform: GitHub
• Original Title: real-time-mlops-platform
• Original Link: https://github.com/rajaka43/real-time-mlops-platform
• Publication Date: 2026-05-27

Transforming machine learning models from lab prototypes into stable services in production environments is one of the core challenges facing the AI field today. The traditional "train once, deploy long-term" model can no longer meet business needs—data distributions change over time, model performance gradually degrades, and manual maintenance consumes a lot of human resources.

MLOps (Machine Learning Operations) emerged as the times require; it draws on the concepts of DevOps and introduces automation, monitoring, and continuous integration/continuous deployment (CI/CD) into the machine learning lifecycle. A well-rounded MLOps platform needs to address the following key issues: How to achieve low-latency real-time prediction? How to detect model degradation and automatically trigger retraining? How to ensure the traceability of code and model versions?

This project provides an end-to-end reference implementation of a production-grade MLOps platform, whose core architecture is built around the following components:

An asynchronous API service built on FastAPI, capable of completing a single prediction within 50 milliseconds (P95 latency). This performance level is crucial for business scenarios requiring immediate responses (such as fraud detection, recommendation systems). The API supports single and batch prediction modes; the batch interface can process up to 1000 records at a time.

The platform has built-in four mechanisms to trigger retraining, ensuring the model always maintains optimal performance:

• Data Drift Detection: Monitor feature distribution changes using three statistical methods: Kolmogorov-Smirnov test, PSI (Population Stability Index), and Jensen-Shannon divergence. When drift is detected three consecutive times, the retraining process is automatically triggered.
• Scheduled Retraining: Perform routine retraining every Sunday at 2 AM UTC to ensure regular model updates.
• Performance Threshold Monitoring: Trigger retraining when the model accuracy (based on real labels from the feedback loop) falls below the set threshold.
• Manual Trigger: Provide an API endpoint for operation and maintenance personnel to start retraining on demand.

Integrate MLflow to implement experiment tracking, recording parameters, metrics, and output artifacts for each training session. The model registry uses versioned management and supports the model promotion process between staging (pre-release) and production environments. Only models that pass the quality gate (accuracy improvement ≥0.5%) are automatically promoted to the production environment.

The platform uses three complementary statistical methods to detect data drift, each measuring distribution changes from a different perspective:

Method	Detection Target	Threshold
Kolmogorov-Smirnov Test	Distribution Shape Change	p < 0.05
PSI (Population Stability Index)	Feature Distribution Shift	PSI > 0.2
Jensen-Shannon Divergence	Probability Distribution Difference	JS > 0.1

This multi-method fusion strategy improves the reliability of drift detection and reduces the false positive rate.