There are 1 repository under ml-pipeline topic.
An open-source ML pipeline development platform
Репозиторий направления Production ML, весна 2021
From data gathering to model deployment. Complete ML pipeline using Docker, Airflow and Python.
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 🚀
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.
Optimizing an ML Pipeline in Azure - A Machine Learning Engineer Project
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.
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.
Showcase of MLflow capabilities
📅 A demo about versioning data and tracking ML experiments using DVC and Mlflow respectively.
This project demonstrates the implementation of a ML pipeline and CI/CD using data on heart strokes. The pipeline includes data preprocessing, model training and evaluation, and deployment. The project leverages GitHub for version control and integration with GitHub actions for efficient and automated model updates.
Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production
Multi Cloud Model Management System for Machine Learning
Sample Airflow ML Pipelines
Disaster response project containing web app, ETL, ML pipelines
A flask api for text-classification with sklearn pipelines.
This project, in collaboration with Figure Eight as part of Udacity's Data Science Nanodegree program, focuses on real-time message categorization for disaster events. It involves an ETL pipeline, ML pipeline, and Web app for classifying disaster response messages.
spark.ml.transformer that join input dataset with external data using Spatial Join
ML api predict house price wrapped in Docker and deployed to AWS ECS/Fargate | #DE |#ML
Dicoding Submission MLOps Fake News Classification using ML Pipeline
Cira set in production
Creating an end-to-end machine learning pipeline, implementing experiment tracking with MLflow, and performing hyperparameter optimization using Optuna.
This machine learning pipeline project aims to develop an ML model to identify customer sentiment from French-language tweets on social media.
This project is a full machine learning pipeline for Star/Galaxy classification using the SDSS dataset. It also contains a detailed report on the development and a DockerFile to easily replicate the results.