An open-source ML pipeline development platform
-
Updated
Dec 11, 2024 - Python
An open-source ML pipeline development platform
Opt-Out tool to check Copyright reservations in a way that even machines can understand.
Репозиторий направления Production ML, весна 2021
A library to accelerate ML and ETL pipeline by connecting all data sources
2 Lines of code to track ML experiments + EDA + check into Github
A curated list of awesome open source tools and commercial products that will help you manage machine learning and data-science workflows and pipelines 🚀
A curated list of awesome open source tools and commercial products that will help you train, deploy, monitor, version, scale, and secure your production machine learning on kubernetes 🚀
From data gathering to model deployment. Complete ML pipeline using Docker, Airflow and Python.
RFlow - A workflow framework for agile machine learning
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
Repo containing Channel Quality Indicator (CQI) data from real car routes in Greece. It contains a reproducable notebook with the implementation of a Bidirectional LSTM Neural Network for real-time CQI forecasting in heterogeneous ultra-dense beyond-5G networks.
Our goal with this ML pipeline template is to create a user friendly utility to drastically speed up the development and implementation of a machine learning model for all sorts of various problems.
Optimizing an ML Pipeline in Azure - A Machine Learning Engineer Project
A package of utilities for engineering ML pipelines.
Install Airflow using docker
The Anonymous Synthesizer for Health Data
This project is part of the Udacity Azure ML Nanodegree. In this project, we use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. We also create, publish, and consume a pipeline.
📅 A demo about versioning data and tracking ML experiments using DVC and Mlflow respectively.
Showcase of MLflow capabilities
Add a description, image, and links to the ml-pipeline topic page so that developers can more easily learn about it.
To associate your repository with the ml-pipeline topic, visit your repo's landing page and select "manage topics."