Waikato/moa
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
This framework helps data scientists and machine learning engineers analyze very large, continuously flowing datasets in real time. It takes in live data streams and applies various machine learning techniques like classification, clustering, or anomaly detection to identify patterns or make predictions as data arrives. You would use this if you need to process and learn from an endless stream of data, such as sensor readings or financial transactions.
654 stars.
Use this if you need to perform real-time machine learning on massive, continuous data streams that are too large to store or process all at once.
Not ideal if your data is static, fits into traditional databases, or if you primarily work with batch processing rather than real-time streams.
Stars
654
Forks
368
Language
Java
License
GPL-3.0
Category
Last pushed
Dec 19, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Waikato/moa"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
oracle/tribuo
Tribuo - A Java machine learning library
o19s/elasticsearch-learning-to-rank
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
Waikato/meka
Multi-label classifiers and evaluation procedures using the Weka machine learning framework.
allegro/allRank
allRank is a framework for training learning-to-rank neural models based on PyTorch.
punit-naik/MLHadoop
This repository contains Machine-Learning MapReduce codes for Hadoop which are written from...