Overview

Next Generation Experimental Tracking for Machine Learning Operations Experiment tracking for machine learning mlop is a Machine Learning Operations (MLOps) framework. It provides self-hostable superior experimental tracking capabilities and lifecycle management for training ML models. To get started, try out our introductory notebook or get an account with us today! The project has 371 GitHub stars and is licensed under Apache-2.0.

Getting Started

Source: GitHub README

%pip install -Uq "mlop[full]"
import mlop

mlop.init(project="hello-world")
mlop.log(\{"e": 2.718\})
mlop.finish()

git clone --recurse-submodules https://github.com/mlop-ai/server.git; cd server
cp .env.example .env
sudo docker-compose --env-file .env up --build

Normalized Features

Source: tool-features-normalized.json

docker, docker compose, pip.

Deploy

Docker

Self-host with Docker Compose

Coolify

One-click via Coolify panel

Elestio

Managed hosting from $9/mo

PikaPods

Simple managed hosting

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