This project demonstrates a basic MLOps pipeline using Docker, Kubernetes, and Pyenv to manage Python environments. It includes a classification model trained with the Iris dataset.

simple project to demonstrate MLOps workflow

This project demonstrates MLOps principles by building and deploying an Iris Classification API using FastAPI, Docker, and Machine Learning. The API allows users to send feature inputs and receive predictions based on the Iris dataset.ned on the class

MLOps Assignment 1: Iris flower classification with Logistic Regression, SVM, and KNN. Includes MLflow tracking (UI, metrics, artifacts).

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Simple MLOps CI/CD pipeline for an Iris classification model.

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End-to-end MLOps pipeline for Iris classification with CircleCI, GCP, Kubernetes, Kubeflow.

An end-to-end MLOps pipeline for training, deploying, and serving an Iris classification model using Docker, GitHub Actions, and Google Kubernetes Engine. Website

Iris dataset classification mlops iitm

MLOps pipeline for Iris classification

MLOps baseline project - Iris classification

Mlops Pipelline for Iris flower classification

FastAPI MLOps Lab - Iris Classification API

Build a iris classification model with mlops

End-to-End MLOps Pipeline for Iris Classification

An MLOps pipeline for the Iris classification model

MLOps pipeline for Iris Classification using AWS SageMaker

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A simple ML project for Iris Flower classification using MLOps

End-to-end MLOps pipeline for Iris classification using Logistic Regression and Random Forest, with complete integration of data versioning, model tracking (MLflow), API deployment (FastAPI), Docker, CI/CD via GitHub Actions, and monitoring.

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ML Ops pipeline using IRIS dataset

MLOPS_ASSIGNMENT

Building a minimal but complete MLOps pipeline for an ML model using a well-known open dataset.

Iris classification, end-to-end, with DevOps and MLOps

Projet MLOps complet - Classification Iris avec pipeline CI/CD

Use metaflow to demo the iris classification MLOps pipeline.

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