Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.

Visualise your Kedro data and machine-learning pipelines and track your experiments.

A simple & elegant experiment tracking framework that integrates persistence logic & best practices directly into Python

Code for Kaggle and Offline Competitions

A Clojure machine learning library

SEML: Slurm Experiment Management Library

PyTorch Template for DL projects

Experiment tracking server focused on speed and scalability

Deploy MLflow with HTTP basic authentication using Docker

GitHub Action That Retrieves Model Runs From Weights & Biases

MLOps for deploying a Credit Risk model

Detect keypoints at a football pitch

Python’s delightfully modular experiment tracker for MLOps

An end to end ML project. Using MLflow for experiment tracking and model registry. Prefect for workflow orchestration. S3 for artifacts storage. AWS Lambda/ ECR for serverless model serving. AWS REST API gateway as endpoint to lambda function. GitHub Actions for CI/CD.

A framework that provides high-level wrappers for effortless experiment launch, enhanced reproducibility, precise control, and unparalleled flexibility.

A demonstration of how DVC and MLFlow can be used in the task of data relabeling

create a robust, simple, effecient, and modern end to end ML Batch Serving Pipeline Using set of modern open-source/free Platforms/Tools

Lightning Talk about sacred at PyData Berlin