Ready to run docker-compose configuration for ML Flow with Mysql and Minio S3

a docker image of the MLflow server component

MLflow Tracking Server with basic auth deployed in AWS App Runner.

This repository provides an example of dataset preprocessing, GBRT (Gradient Boosted Regression Tree) model training and evaluation, model tuning and finally model serving (REST API) in a containerized environment using MLflow tracking, projects and models modules.

Fully reproducible, Dockerized, step-by-step, tutorial on training and serving a simple sklearn classifier model using mlflow. Detailed blog post published on Towards Data Science.

Mlflow Docker Image

mlflow container setup for docker, docker compose and kubernetes including helm chart

This project creates a basic web service for solving image-based CAPTCHAs. Using the Flask framework, it allows users to upload CAPTCHA images and employs an Optical Character Recognition (OCR) pipeline to extract the embedded text.

Kubeflow Pipeline along with MLflow Tracking on a time series forecasting example.

MLflow example to track Parameters and Metrics by using MLproject Functionality

MLflow setup using Docker and AWS S3

Launch an MLFlow server through Docker

Run tidyverse, tidymodels, targets, carrier, and MLFlow within Docker

🌐 Language identification for Scandinavian languages

Some examples of running R in a Docker container with machine learning and MLOps features

Testing the integration of MLFlow and BentoML

MLflow deployment

Host MLFlow Tracking Server and Model Registry as a containerized application on Kubernetes

This repository provides a foundational guide to MLOps, including tools and workflows for model versioning, data versioning, CI/CD pipelines, and experiment tracking. It features examples and use cases in Python, Jupyter Notebook, and Google Colab, along with integration with DagsHub for collaborative machine learning.

Deploy MLFlow Tracking Server with Docker Compose