Introduction to MLflow with a demo locally and how to set it on AWS

MLflow on AWS Fargate integrated with Amazon SageMaker.

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.

An end-to-end machine learning (mlops) project

Mlflow Docker Image

MLflow is Open source platform for the machine learning lifecycle so here you can learn MLflow End to End Example with Prediction.

MLFlow End to End Workshop at Chandigarh University

Basic wrapper to allow logging to MLFlow from Julia

Classifying asteroids based on NASA JPL data records

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.

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

Experiment tracking with MLFlow.

This project(RAG) focuses on operationalizing LLMs by integrating OpenAI, MLflow, FastAPI, and RAGAS for evaluation. It allows users to deploy and manage LLMs, track model runs, and log evaluation metrics in MLflow. The project also features MLflow traces that logs all the user inputs ,responses ,retrieved contexts ,and other essential metrices.

Training a YOLOv8 model for wildfire smoke detection.

TechCon Experimentation with MLFlow and Dask

Testing deployment of PyMC models using MLFlow and BentoML.

This is an end-to-end animal face classification model with Keras, KerasTuner, Mlflow, SQLite, Streamlit, and FastAPI which can classify animal faces as either cat, dog or wildlife

Using a stack of powerful tools to build an End-to-End AutoML pipeline for insurance cross-sell prediction

The MLflow TensorFlow Guide is an educational project. This project demonstrates how to build, train, and manage a TensorFlow machine learning model using MLflow, a powerful open-source platform for the end-to-end machine learning lifecycle.

Intent Classification with Hugging Face, Mlfow experiment tracking, Behavioural testing of models with checklist