[UNMAINTAINED] Automated machine learning for analytics & production

(AAAI' 20) A Python Toolbox for Machine Learning Model Combination

Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo

Primitives for machine learning and data science.

Provenance and caching library for python functions, built for creating lightweight machine learning pipelines

Wind Power Forecasting using Machine Learning techniques.

kubeflow example

Python library for Executable Machine Learning Knowledge Graphs

create a robust, simple, effecient, and modern end to end ML Batch Serving Pipeline Using set of modern open-source/free Platforms/Tools

A code-first way to define Ploomber pipelines

Improved pipelines for data science projects.

Sentiment analysis on customer reviews using machine learning and python

based on the befitting sensors fetched data, prediction is to be made whether the failure in a vehicle is due to APS or some other component. Emphasis is on reducing the consequential cost by reducing the false positives and false negatives and more importantly false negatives as it appears cost incurred due to them is 50 times higher.

ML AutoTrainer Engine, developed using Streamlit, is an advanced app designed to automate the machine learning workflow. It provides a user-friendly platform for data processing, model training, and prediction, enabling a seamless, code-free interaction for machine learning tasks.

Building machine learning pipelines with procedural programming, custom-pipeline or third-party code using the titanic data set from Kaggle

Data Engineering Project of Udacity Data Scientist Nanodegree

Example string processing pipeline on Triton Inference Server

In this project I'm using machine learning Pipeline which is then made into a Flask Application which is then dockerized using docker and then the docker image is deployed on Amazon-Web-Services, Elastic Beanstalk.

Machine Learning pipelines are deployed to accomplish the objective of credit risk analysis.