ml-pipeline
There are 102 repositories under ml-pipeline topic.
DMIA_ProductionML_2021_Spring
Репозиторий направления Production ML, весна 2021
dataligo
A library to accelerate ML and ETL pipeline by connecting all data sources
VevestaX
2 Lines of code to track ML experiments + EDA + check into Github
When-ML-pipeline-meets-Hydra
:cyclone:
Machine-Learning-Pipelines
From data gathering to model deployment. Complete ML pipeline using Docker, Airflow and Python.
awesome-mlops-kubernetes
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 🚀
awesome-ml-pipelines
A curated list of awesome open source tools and commercial products that will help you manage machine learning and data-science workflows and pipelines 🚀
heart-failure-detection
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
rflow
RFlow - A workflow framework for agile machine learning
HetNets-steering
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.
ml-pipeline
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
Optimizing an ML Pipeline in Azure - A Machine Learning Engineer Project
bodywork-pipeline-utils
A package of utilities for engineering ML pipelines.
airflow-docker
Install Airflow using docker
DVC-Mlflow-pipeline
📅 A demo about versioning data and tracking ML experiments using DVC and Mlflow respectively.
ASyH
The Anonymous Synthesizer for Health Data
ML_pipeline
Machine Learning Project Pipeline
Operationalizing-Machine-Learning-in-Azure
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.
mlflow_showcase
Showcase of MLflow capabilities
Disaster_Response_Pipeline
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.
ml-pipeline-using-stroke-data
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.
fake-news-classification
Dicoding Submission MLOps Fake News Classification using ML Pipeline
mlops
Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production
sklearn-svm-classification-pipeline-api
A flask api for text-classification with sklearn pipelines.
airflow_ml_ops
Sample Airflow ML Pipelines
xmodel
Multi Cloud Model Management System for Machine Learning
HousePricePredAPI
ML api predict house price wrapped in Docker and deployed to AWS ECS/Fargate | #DE |#ML
spark.ml.SpatialJoinTransformer
spark.ml.transformer: join two datasets using spatial relations
udacity-disaster-response
Disaster response project containing web app, ETL, ML pipelines
grazing-cira
Cira set in production
ml-pipeline
Creating an end-to-end machine learning pipeline, implementing experiment tracking with MLflow, and performing hyperparameter optimization using Optuna.
French-Twitter-Sentiment-Analysis
This machine learning pipeline project aims to develop an ML model to identify customer sentiment from French-language tweets on social media.
AMLS-Star-Galaxy-Classification
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.
Generating-Composite-Proxy-Target-Variable-for-Machine-Learning-Models-of-Business-Decisions
A multi-criteria decision analysis (MCDA)-based composite proxy target variable generation technique for business decision modeling that uses relevant features or independent variables conceptually related to the intended target variable.
MLOPs_with_kedro
simple MLOPs demo with kedro..