Introduction to MLflow with a demo locally and how to set it on AWS
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Updated
Mar 7, 2021 - Jupyter Notebook
Introduction to MLflow with a demo locally and how to set it on AWS
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.
MLflow is Open source platform for the machine learning lifecycle so here you can learn MLflow End to End Example with Prediction.
Mlflow Docker Image
MLFlow End to End Workshop at Chandigarh University
Classifying asteroids based on NASA JPL data records
An end-to-end machine learning (mlops) project
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.
TechCon Experimentation with MLFlow and Dask
Using a stack of powerful tools to build an End-to-End AutoML pipeline for insurance cross-sell prediction
Intent Classification with Hugging Face, Mlfow experiment tracking, Behavioural testing of models with checklist
Training a YOLOv8 model for wildfire smoke detection.
Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow
Production Level MLOps Project for Titanic Dataset
This repository showcases machine learning experiment using MLflow, a powerful open-source platform for managing the end-to-end machine learning lifecycle. The experiment is designed to demonstrate best practices for tracking and managing machine learning projects, including experiment tracking.
Some examples of running R in a Docker container with machine learning and MLOps features
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