Automated ML pipeline

This is a Bank Marketing Machine Learning Classification Project in fulfillment of the Udacity Azure ML Nanodegree. In this project, you will learn to utilize Azure Machine Learning Studio and Azure Python SDK to create classifier models from scratch. The files and documentation with experiment instructions needed for replicating the project is …

Shrinkit is a powerful GUI-based Python library designed for automating machine learning tasks. With its intuitive interface, Shrinkit simplifies the process of building, training, and evaluating machine learning models, making it accessible to users of all skill levels. Shrinkit is a No-code package which can be used as a GUI.

AutoML

Utilizes pycaret to automates machine learning workflows (Deployed at streamlit)

Auto Machine learning platform as seen on https://www.youtube.com/watch?v=JHJLLiMnz6A

Projeto de criação de modelo de machine learning para score de credito, percorrendo todo o pipeline dos dados. Coleta, exploração, tratamento, limpeza, treino e deploy.

Automating the ML Training Lifecycle with MLxOPS

This library aims at providing tools for an automatic machine learning approach. As many tools already exist to establish one or the other component of an AutoML approach, the idea of this library is to provide a structure rather than to implement a complete service.

This project aims to create Machine Learning models using Azure's AutoML to find the best model that fits the data and Hypderdrive to find the best hyperparameters.

AutoML as a Service.

Knowledge-driven AutoML

A GitHub compiling the input data, Python and Jupyter Notebook scripts, and all relevant statistical outputs from running the AutoMLPipe-BC automated machine learning pipeline (from the Urbanowicz Lab - https://github.com/UrbsLab) on a large-scale single nucleotide polymorphism (SNP) dataset from patients with congenital heart disease (CHD)

TinyAutoML is a comprehensive Pipeline Classifier Project thought as a Scikit-learn plugin

Powerful AutoML toolkit

Sugar candy for data scientist. Easy manipulation in time-series data analytics works.

Auto torch image models: train and evaluation

Benchmark pipeline for evaluating language models on financial tasks, including sentiment analysis and credit scoring. Supports over ten tasks with modular design for easy integration of new tasks. Provides automated performance metrics for standardized evaluation, benefiting researchers and practitioners in finance.

Atlantic: Automated Data Preprocessing Framework for Supervised Machine Learning

Library for streaming data and incremental learning algorithms.