A package that makes it trivial to create and evaluate machine learning pipeline architectures.

[KDD 2023] Deep Pipeline Embeddings for AutoML

TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. In this video, I'll show you how you can use TPOT for Classification.

⚡🤵🏻‍♂️ AI-powered Jenkins optimizer that analyzes pipelines, identifies bottlenecks, and suggests optimizations to reduce build times by up to 50% 👨🏻‍💻🚀

Meet the beast of Jenkins MCP with AI-powered diagnostics, and more with Industrial debugging on steroids, AI ripping through failure logs, vector search that thinks faster than you, and multi-instance chaos turned into orchestrated genius. CI/CD will never be the same.]

AutostreamPipe: LLM Assisted Automatic Stream Processing Pipeline Generation

This project is for understanding and quantifying the errors in a machine learning or data analytic pipeline. Two approaches are explored. The first is using freezing and unfreezing of pipeline components (using optimization techniques like grid-search, random-search, Bayesian Optimization, Genetic Algorithms etc.). The second is using a gradien…

This repository contains code for building a machine learning model to predict NFL players' selection for the Pro Bowl based on player statistics from the NFL Pro Bowl 2022 dataset. Three different supervised learning models, including one linear and two non-linear models, are implemented.