> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/problem-framing/online-learning.md).
# Online Learning
Some models cannot wait for a finished dataset: they must update as labeled examples arrive in a stream, including non-stationary settings. The page starts with classical online learning, its tutorials, theory, and tools, and then moves to online deep learning.
The same notes are in [Active Learning Algorithms](/decision-intelligence/active-learning-algorithms.md), [Follow the regularized leader](/decision-intelligence/follow-the-regularized-leader.md), [Incremental Learning](/decision-intelligence/incremental-learning.md), and [Training Strategies](/evals/training-strategies.md).
### Online (Classical) Learning
Classical online learning updates the model as labeled examples arrive in a stream, instead of building one model on the whole dataset at once. If you want to start with OL, [start here](https://dziganto.github.io/data%20science/online%20learning/python/scikit-learn/An-Introduction-To-Online-Machine-Learning/): "An Introduction To Online Machine Learning" begins from batch, or offline, learning, the standard approach you already know, and contrasts it with the online version. Analytics Vidhya's "Introduction to Online Machine Learning: Simplified" is the second introduction, [here](https://www.analyticsvidhya.com/blog/2015/01/introduction-online-machine-learning-simplified-2/).
The theory is Shay Shalev's. [A thesis about online learning](http://ttic.uchicago.edu/~shai/papers/ShalevThesis07.pdf) is his thesis, opening with “How much better to get wisdom than gold, to choose understanding rather than silver”. [Some answers about what is OL,](https://www.quora.com/What-is-the-best-way-to-learn-online-machine-learning) are the Quora answers to "What is the best way to learn online machine learning?", which frame it as models that learn incrementally from data streams and point at production concerns such as latency, concept drift, and evaluation under streaming; the first one actually talks about S.Shalev’s [other paper.](http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf), his online learning survey. Online learning — Andrew Ng — coursera is the lecture version, kept at the end of the page.
From theory to production: [Chip Huyen on online prediction & learning](https://huyenchip.com/2020/12/27/real-time-machine-learning.html) argues that machine learning is going real-time, with a 2022 follow-up on its challenges and solutions. For code, [River](https://github.com/online-ml/river/) is a Python library for [online machine learning](https://www.wikiwand.com/en/Online_machine_learning), and the figure below sketches the setting.
Online machine learning.
### Online Deep Learning (ODL)
After the classical setting, online deep learning adapts neural models from streaming or non-stationary data. The notes here name Hedge back propagation (HDP), Autonomous DL, Qactor — online AL for noisy labeled stream data. The article that covered them is kept at the end of the page.
## Deprecated links
{% hint style="warning" %}
These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above.
{% endhint %}
* Hedge back propagation (HDP), Autonomous DL, Qactor. This address no longer opens:
* coursera. This address no longer opens:
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://www.mlcompendium.com/problem-framing/online-learning.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
