Predicting customer churn repo focusing on the software engineering part
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Updated
May 16, 2021 - Python
Predicting customer churn repo focusing on the software engineering part
An end to end machine learning system with mlflow to predict the failure of engine component
Machine Learning Project which uses DVC for Data versioning, MLFlow for Experiment Tracking, ONNX & Docker file for packaging, Flask for API.
Diagnose and fix problems in a production deployed code using MLflow.
Train, infer and deploy suitable machine learning models for any uploaded dataset, and visualise the experimental results (MLflow) easily from the GUI.
This Repo shows TensorFlow in action. The library is used to perform image classification on the MNIST data set and the ML results are monitored with MLflow
This machine learning pipeline trains a model that aims to predict temperature and precipitation for 10 major cities in UK. The pipeline pulls data from API, process and tests it, trains and test a model and makes a batch prediction for next week's weather.
Simple ML system for the iris problem
An end to end machine learning system with mlflow for detecting the quality of wafer sensors
This project is a POC of an intelligent system (adopting hexagonal architecture) which can serve a deployed model in MLflow both by FastAPI and NATSMessaging entrypoints.
The CORDS ML Resource Manager is an REST API for managing ML assets from MLFlow, integrating with CORDS-local-digital-thread and IDSA Data Applications. It offers comprehensive ML model management and enhances semantic organization through CORDS-Semantic-lib, ensuring efficient cataloging and accessibility of ML artifacts.
Create ML pipelines to facilitate the Machine Learning Life Cycle.
Add a description, image, and links to the mlflow topic page so that developers can more easily learn about it.
To associate your repository with the mlflow topic, visit your repo's landing page and select "manage topics."