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!

🎥 Demo

mlop adopts a KISS philosophy that allows it to outperform all other tools in this category. Supporting high and stable data throughput should be THE top priority for efficient MLOps.

mlop logger (bottom left) v. a conventional logger (bottom right)

🚀 Getting Started

• Try mlop on our platform in a notebook & start integrating in just 5 lines of Python code:

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

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

• Self-host your very own mlop instance & get started in just 3 commands with docker-compose

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

You may also learn more about mlop by checking out our documentation.

You can try everything out in our introductory tutorial and torch tutorial.

🫡 Vision

mlop is a platform built for and by ML engineers, supported by our community! We were tired of the current state of the art in ML observability tools, and this tool was born to help mitigate the inefficiencies - specifically, we hope to better inform you about your model performance and training runs; and actually save you, instead of charging you, for your precious compute time!

🌟 Be sure to star our repos if they help you ~