ml-pipelines

An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈

An open-source ML pipeline development platform

Open source AI platform for rapid development of advanced AI and AGI pipelines.

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

An AutoML pipeline selection system to quickly select a promising pipeline for a new dataset.

Free and open source automation platform

Best practices for engineering ML pipelines.

Library for streaming data and incremental learning algorithms.

Components that I have created for Kubeflow Pipelines. Try them in https://cloud-pipelines.net/pipeline-editor/

Serverless ML system to predict the direction and volume of electricity flows to and from the Netherlands and its energy transmission partners.

This Project is a part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The initial dataset contains pre-labelled tweet and messages from real-life disasters. The aim of this project is to build a Natural Language Processing tool that categorize messages.

A collection of real-world machine learning and AI projects. Explore hands-on implementations of cutting-edge models, practical solutions, and techniques to tackle real-world challenges using AI.

This a repo that was created to learn more about Airflow and develop awesome data engineering projects. 🚀🚀

Fraud detection ML pipeline and serving POC using Dask and hopeit.engine. Project created with nbdev: https://www.fast.ai/2019/12/02/nbdev/

🧠A hands-on workspace for practicing machine learning concepts, data preprocessing, and experimenting with small ML projects. This repo includes foundational Python scripts, real-world mini-projects, and experiments that reflect a progressive learning journey in applied machine learning.

This repository contains my code solution to DeepLearning.AIs Practical Data Science On AWS Cloud Specialization.

Big data application of Machine Learning concepts for sentiment classification of US Airlines tweets. The focus is on the usage of pyspark libraries (ml-lib) on big data to solve a problem using Machine Learning algorithms and not about the choice of algorithm used in the ML model creation. It also involves data pre-processing using NLP techniques, cross-validation and parameter-grid builder.

ML pipeline to categorize emergency messages based on the needs communicated by the sender.

Develop algorithms to classify genetic mutations based on clinical evidence (text).

Guide on how to structure and implement machine learning pipelines.