Popular GitHub Repositories for Kedro

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

A kedro-plugin for integration of mlflow capabilities inside kedro projects (especially machine learning model versioning and packaging)

A tutorial on how to use kedro-mlflow plugin (https://github.com/Galileo-Galilei/kedro-mlflow) to synchronize training and inference and serve kedro pipeline

Kedro plugin to support running workflows on Microsoft Azure ML Pipelines

Kedro Plugin to support running workflows on GCP Vertex AI Pipelines

Wind Power Forecasting using Machine Learning techniques.

Using ChatGPT to build a Kedro ML pipeline and Streamlit frontend

Template for multi-modal machine learning in healthcare using Kedro. Combine reports, tabular data and images using various fusion methods.

Cryptocurrency forecasting ? training and serving models made automatic

A kedro plugin that enables logging to the ml experiment tracker aim

Vision framework which brings a more robust, Deep Learning-based approach to some usual OpenCV use cases

This repo showcases a project that transforms ML model training into a simplified, production-ready Kedro Dockerized Pipeline. It emphasizes best MLOps practices, enabling easy training, evaluation, and deployment of models, including XGBoost, LightGBM and Random Forest, with built-in visualization and logging features for effective monitoring.

get started around the Machine Learning management tools

MLFLow Server - Jupyter Notebook Dockerized StarterKit

Churn Prediction with Kedro, Kedro-Viz, and Kedro-Mlflow ?? ?. PowerBI Dashboard ? also included. Kedro? https://kedro.org/

ASR: Light deep net for real-time voice activity detection

This repository showcases a case study to improve customer service via ML. It includes a guide on setting up an end-to-end ML project using MLOps best practices to ensure scalability and maintainability. The key features include a robust CI/CD/CT pipeline for continuous integration, delivery, and training, and deploying trained model as a REST API.

A pipeline using Kedro to orchestrate the deployment of a deep learning transformer model for classifying toxic comments. This project integrates data preprocessing, model training, and deployment into a streamlined and reproducible workflow, enabling efficient handling of the toxic comment classification problem in NLP.

End-to-end pipeline to extract logs from Brawlstars API, using a KMeans model for player classification and cohorts creation, to produce parametrized bounded retention metrics loading them to a Google Cloud Bucket.

Kedro pipelines for multimodal ML in TensorFlow.