Monitoring machine learning models in production using Evidently.

Development, deployment and monitoring of machine learning models following the best MLOps practices

Build End to End ML pipeline for USVisa prediction, deploy web App to AWS Ec2 instance using Docker, CI/CD with github actions

Learn how to handle model drift and perform test-based model monitoring

mlops zoomcamp 2023 solutions

Final Project of the MLOps Zoomcamp hosted by DataTalksClub.

Comparison between several Python data profile libraries.

Demonstração de análise via dashboard interativo feito com a lib Evidently.

This is a simple http server to manage feature flags, compatible with Amazon CloudWatch Evidently.

Github Repository to implement Kubeflow with Evidently.ai for Model Monitoring

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.

Sample for creating a CloudWatch Evidently project

White and Red Wine classification using logistic regression

Minimal example of Evidently + Streamlit

This an attempt to predict fraud transactions from a huge collection of records of bank transaction over a period of time.

MLOps Zoomcamp hosted by DataTalksClub.

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.

End to End toy example of MLOps