Project: End-to-End MLOps Pipeline
30 Days of MLOps Challenge: Day 26
By Sandip Das · · 10 min read
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Hey — It's Sandip Das 👋
In this project you will learn how to build a complete production-ready machine learning pipeline with containerized microservices architecture, implementing end-to-end monitoring using Prometheus metrics integration and Grafana dashboards for real-time observability of model performance, API health, and system resources.
This project demonstrates critical MLOps skills including Docker orchestration with docker-compose, FastAPI model serving with custom Prometheus middleware for tracking HTTP requests and model predictions, MLflow experiment tracking while maintaining data persistence and proper configuration management across development and production environments.
In this project you will learn how to build a complete production-ready machine learning pipeline with containerized microservices architecture, implementing end-to-end monitoring using Prometheus metrics integration and Grafana dashboards for real-time observability of model performance, API health, and system resources.
This project demonstrates critical MLOps skills including Docker orchestration with docker-compose, FastAPI model serving with custom Prometheus middleware for tracking HTTP requests and model predictions, MLflow experiment tracking while maintaining data persistence and proper configuration management across development and production environments.
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New to MLOps ? Check out => 30 Days of MLOps
Read the full article on learnxops.com.
Topics: Project, MLOps, Community, 30 Days of MLOps