Awesome Online Machine Learning / Courses and books |
| Machine Learning for Streaming Data with Python | 68 | about 1 year ago | |
| IE 498: Online Learning and Decision Making | | | |
| Introduction to Online Learning | | | |
| Machine Learning the Feature | | | — Gives some insights into the inner workings of Vowpal Wabbit, especially the |
| Machine learning for data streams with practical examples in MOA | | | |
| Online Methods in Machine Learning (MIT) | | | |
| Streaming 101: The world beyond batch | | | |
| Prediction, Learning, and Games | | | |
| Introduction to Online Convex Optimization | | | |
| Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions | | | — The entire book builds upon Online Learning paradigm in applied learning/optimization problems, being the reference |
| Big Data course at the CILVR lab at NYU | | | — Focus on linear models and bandits. Some courses are given by John Langford, the creator of Vowpal Wabbit |
| Machine Learning for Personalization | | | — Course from Columbia by Tony Jebara, covers bandits |
| An Introduction to Online Learning | | | |
| Streaming Data Analytics | | | Course from Politecnico di Milano |
Awesome Online Machine Learning / Blog posts |
| Fennel AI blog posts about online recsys | | | |
| Anomaly Detection with Bytewax & Redpanda (Bytewax, 2022) | | | |
| The online machine learning predict/fit switcheroo (Max Halford, 2022) | | | |
| Real-time machine learning: challenges and solutions (Chip Huyen, 2022) | | | |
| Anomalies detection using River (Matias Aravena Gamboa, 2021) | | | |
| Introdução (não-extensiva) a Online Machine Learning (Saulo Mastelini, 2021) | | | |
| Machine learning is going real-time (Chip Huyen, 2020) | | | |
| The correct way to evaluate online machine learning models (Max Halford, 2020) | | | |
| What is online machine learning? (Max Pagels, 2018) | | | |
| What Is It and Who Needs It (Data Science Central, 2015) | | | |
Awesome Online Machine Learning / Software / Modelling |
| River | 5,102 | 4 days ago | — A Python library for general purpose online machine learning |
| dask | | | |
| Jubatus | | | |
| Flink ML | | | Apache Flink machine learning library |
| LIBFFM | | | — A Library for Field-aware Factorization Machines |
| LIBLINEAR | | | — A Library for Large Linear Classification |
| LIBOL | | | — A collection of online linear models trained with first and second order gradient descent methods. Not maintained |
| MOA | | | |
| scikit-learn | | | — of scikit-learn's estimators can handle incremental updates, although this is usually intended for mini-batch learning. See also the page |
| Spark Streaming | | | — Doesn't do online learning per say, but instead mini-batches the data into fixed intervals of time |
| SofiaML | | | |
| StreamDM | 492 | over 1 year ago | — A machine learning library on top of Spark Streaming |
| Tornado | 127 | about 1 year ago | |
| VFML | | | |
| Vowpal Wabbit | 8,490 | about 2 months ago | |
Awesome Online Machine Learning / Software / Deployment |
| KappaML | | | |
| django-river-ml | 10 | 11 months ago | — a Django plugin for deploying River models |
| chantilly | 97 | over 2 years ago | — a prototype meant to be compatible with River (previously Creme) |
Awesome Online Machine Learning / Papers / Linear models |
| Field-aware Factorization Machines for CTR Prediction (2016) | | | |
| Practical Lessons from Predicting Clicks on Ads at Facebook (2014) | | | |
| Ad Click Prediction: a View from the Trenches (2013) | | | |
| Normalized online learning (2013) | | | |
| Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent (2011) | | | |
| Dual Averaging Methods for Regularized Stochastic Learning andOnline Optimization (2010) | | | |
| Adaptive Regularization of Weight Vectors (2009) | | | |
| Stochastic Gradient Descent Training forL1-regularized Log-linear Models with Cumulative Penalty (2009) | | | |
| Confidence-Weighted Linear Classification (2008) | | | |
| Exact Convex Confidence-Weighted Learning (2008) | | | |
| Online Passive-Aggressive Algorithms (2006) | | | |
| Logarithmic Regret Algorithms forOnline Convex Optimization (2007) | | | |
| A Second-Order Perceptron Algorithm (2005) | | | |
| Online Learning with Kernels (2004) | | | |
| Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms (2004) | | | |
Awesome Online Machine Learning / Papers / Support vector machines |
| Pegasos: Primal Estimated sub-GrAdient SOlver for SVM (2007) | | | |
| A New Approximate Maximal Margin Classification Algorithm (2001) | | | |
| The Relaxed Online Maximum Margin Algorithm (2000) | | | |
Awesome Online Machine Learning / Papers / Neural networks |
| Three scenarios for continual learning (2019) | | | |
Awesome Online Machine Learning / Papers / Decision trees |
| AMF: Aggregated Mondrian Forests for Online Learning (2019) | | | |
| Mondrian Forests: Efficient Online Random Forests (2014) | | | |
| Mining High-Speed Data Streams (2000) | | | |
Awesome Online Machine Learning / Papers / Unsupervised learning |
| Online Clustering: Algorithms, Evaluation, Metrics, Applications and Benchmarking (2022) | | | |
| Online hierarchical clustering approximations (2019) | | | |
| DeepWalk: Online Learning of Social Representations (2014) | | | |
| Online Learning with Random Representations (2014) | | | |
| Online Latent Dirichlet Allocation with Infinite Vocabulary (2013) | | | |
| Web-Scale K-Means Clustering (2010) | | | |
| Online Dictionary Learning For Sparse Coding (2009) | | | |
| Density-Based Clustering over an Evolving Data Stream with Noise (2006) | | | |
| Knowledge Acquisition Via Incremental Conceptual Clustering (2004) | | | |
| Online and Batch Learning of Pseudo-Metrics (2004) | | | |
| BIRCH: an efficient data clustering method for very large databases (1996) | | | |
Awesome Online Machine Learning / Papers / Time series |
| Online Learning for Time Series Prediction (2013) | | | |
Awesome Online Machine Learning / Papers / Drift detection |
| A Survey on Concept Drift Adaptation (2014) | | | |
Awesome Online Machine Learning / Papers / Anomaly detection |
| Leveraging the Christoffel-Darboux Kernel for Online Outlier Detection (2022) | | | |
| Interpretable Anomaly Detection with Mondrian Pólya Forests on Data Streams (2020) | | | |
| Fast Anomaly Detection for Streaming Data (2011) | | | |
Awesome Online Machine Learning / Papers / Metric learning |
| Online Metric Learning and Fast Similarity Search (2009) | | | |
| Information-Theoretic Metric Learning (2007) | | | |
| Online and Batch Learning of Pseudo-Metrics (2004) | | | |
Awesome Online Machine Learning / Papers / Graph theory |
| DeepWalk: Online Learning of Social Representations (2014) | | | |
Awesome Online Machine Learning / Papers / Ensemble models |
| Optimal and Adaptive Algorithms for Online Boosting (2015) | | | — An implementation is available |
| Online Bagging and Boosting (2001) | | | |
| A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997) | | | |
Awesome Online Machine Learning / Papers / Expert learning |
| On the optimality of the Hedge algorithm in the stochastic regime | | | |
Awesome Online Machine Learning / Papers / Active learning |
| A survey on online active learning (2023) | | | |
Awesome Online Machine Learning / Papers / Miscellaneous |
| Multi-Output Chain Models and their Application in Data Streams (2019) | | | |
| A Complete Recipe for Stochastic Gradient MCMC (2015) | | | |
| Online EM Algorithm for Latent Data Models (2007) | | | — Source code is available |
| StreamAI: Dealing with Challenges of Continual Learning Systems for Serving AI in Production (2023) | | | |
Awesome Online Machine Learning / Papers / Surveys |
| Machine learning for streaming data: state of the art, challenges, and opportunities (2019) | | | |
| Online Learning: A Comprehensive Survey (2018) | | | |
| Online Machine Learning in Big Data Streams (2018) | | | |
| Incremental learning algorithms and applications (2016) | | | |
| Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data | | | |
| Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey (2011) | | | |
| Online Learning and Stochastic Approximations (1998) | | | |
Awesome Online Machine Learning / Papers / General-purpose algorithms |
| Maintaining Sliding Window Skylines on Data Streams (2006) | | | |
| The Sliding DFT (2003) | | | — An online variant of the Fourier Transform, a concise explanation is available |
| Sketching Algorithms for Big Data | | | |
Awesome Online Machine Learning / Papers / Hyperparameter tuning |
| ChaCha for Online AutoML (2021) | | | |
Awesome Online Machine Learning / Papers / Evaluation |
| Delayed labelling evaluation for data streams (2019) | | | |
| Efficient Online Evaluation of Big Data Stream Classifiers (2015) | | | |
| Issues in Evaluation of Stream Learning Algorithms (2009) | | | |
awesome-online-machine-learning 
Online machine learning resources
jnv/lists
josephreisinger/vowpal_porpoise
bigartm/bigartm
balavenkatesh3322/model_deployment
ahkarami/deep-learning-in-production
pyo3/pyo3
zenkay/bigdata-ecosystem
astral-sh/ruff