Maternal Health Risk prediction MLOps pipeline
-
Updated
Dec 6, 2022 - Python
Maternal Health Risk prediction MLOps pipeline
Final Project of the MLOps Zoomcamp hosted by DataTalksClub.
End-to-end platform for training, deploying, and monitoring a churn prediction model—built using MLOps best practices and tools applied from the DataTalksClub MLOps Zoomcamp. Project earned the highest-tier score (achieved by 11 out of 200+ cohort participants) in peer-reviewed project assessment.
Online Prediction Machine Learning System designed, deployed and maintained with MLOps Practices. Goal of the project is to predict individuals income based on census data.
An MLOps pipeline for optimizing game discount strategies using Steam reviews, tags, and competitor pricing. Designed for data-driven revenue maximization in the gaming industry.
MLOps Zoomcamp hosted by DataTalksClub.
This an attempt to predict fraud transactions from a huge collection of records of bank transaction over a period of time.
Learn how to handle model drift and perform test-based model monitoring
🌎 🚙📚 Predicting travel times and traffic density on a highway in Slovenia
This project adopts a modular Python architecture within an MLOps framework to enhance subscription renewal predictions, utilizing FastAPI and MongoDB with AWS integration (S3, ECR, EC2). Docker ensures seamless deployment, and GitHub Actions automate the CI/CD workflows. Evidently AI monitors drift to guarantee predictive accuracy and reliability.
White and Red Wine classification using logistic regression
An end-to-end machine learning project predicting DoorDash delivery durations, utilizing MLOps principles and best practices.
This project builds an MLOps pipeline using Evidently for monitoring model performance and Prefect for task orchestration. It processes NYC taxi data, stores metrics in PostgreSQL, and visualizes results in Grafana via Docker Compose.
Agent AI tự động giám sát drift dữ liệu trong pipeline ML, cảnh báo qua email và Slack.
Production-style ML monitoring template on the Wine Quality (red) dataset: Evidently (data/target/prediction drift, data quality) + adversarial validation, PSI/JS effect sizes, SHAP/PDP, slice analysis, and an Alert Policy with actions
Add a description, image, and links to the evidently topic page so that developers can more easily learn about it.
To associate your repository with the evidently topic, visit your repo's landing page and select "manage topics."