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
Anomaly Detection Pipeline with Isolation Forest model and Kedro framework
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.
Attendance prediction tool for NBA games using machine learning. Full pipeline implemented in Python from data ingestion to prediction. Attained mean absolute error of around 800 people (about 5% capacity) on test set.
Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"
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.
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
Optimize and Enhance Your Search Quality
DataCamp inetrmediate course on how and when to perform data preprocessing in any machine learning project to get the data ready for modeling
In this project I did Complete EDA, and Build a ML model that can accurately predict whether an Employee will be leave a company or not based on different factors.
Pipeline for augmenting sparse data for genetic optimisation
Developed a ETL pipeline, a ML training pipeline and a Flask web app that can classify disaster-related messages input by a user.
White and Red Wine classification using logistic regression
Develop a machine learning model that can predict whether people have diabetes when their characteristics are specified
The code snippet cleans and analyzes a hotel bookings dataset, handling missing values, dropping unnecessary columns, and creating new features. It visualizes the data using various plots and performs feature encoding and selection. It then trains machine learning models to predict hotel booking cancellations.
Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.
A Python desktop application using CustomTkinter for data analysis and machine learning.
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.
An advanced MLOps project featuring an end-to-end machine learning pipeline with a Random Forest Classifier. This repository automates data preprocessing, model training, hyperparameter tuning, and deployment using CI/CD, containerization, and cloud deployment. It includes real-time model monitoring, data versioning with DVC (Data Version Control)
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