Example machine learning pipeline with MLflow and Hydra
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Updated
Apr 21, 2023 - Python
Example machine learning pipeline with MLflow and Hydra
This project is focused on the Deployment phase of machine learning. The Docker and FastAPI are used to deploy a dockerized server of trained machine learning pipeline.
Anomaly Detection Pipeline with Isolation Forest model and Kedro framework
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
A Python desktop application using CustomTkinter for data analysis and machine learning.
Developed a ETL pipeline, a ML training pipeline and a Flask web app that can classify disaster-related messages input by a user.
A Streamlit-based app that automate app Designed to assist data analysts and ML engineers with end-to-end machine learning workflows.
Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.
In today's fast-paced world, efficient food delivery is crucial. This project presents a robust and modular end-to-end machine learning pipeline designed to predict food delivery times. By leveraging a rich dataset containing delivery personnel details, restaurant locations, order information, and environmental factors like weather and traffic.
Desenvolver uma aplicação de reconhecimento facial que permita verificar a similaridade entre uma imagem de entrada e imagens em uma base de dados (via embeddings), com a possibilidade de adicionar novas faces à base.
Framework3 is a super-simple and robust ML Pipeline for tabular and image competition. The purpose of this is to make the process not too abstract, so that the user can have full control over it.
Classifying real messages that were sent during disaster events so that they can be sent to an appropriate disaster relief agency.
42 school project. Process EEG datas by cleaning, extracting, creating a ML pipeline implementing a dimensionality reduction algorithm before finding the right classifier and handling a real time data-stream with sklearn.
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