Master
Online machine
learning

Traditional machine learning models are trained using predefined data. Online machine learning models instead learn continuously from new data, constantly improving responses and outputs.

Online machine learning (also known as incremental or out-of-core learning) models constantly learn from new inputs, which helps them adapt to changes and emerging trends. That makes them suited to highly dynamic use cases like customer sentiment analysis, decision-making for autonomous vehicles and financial market analysis.

What is it?

Online machine learning models continuously learn from new data in real-time, instead of learning from fixed data.

What’s in it for you?

Online machine learning enables you to move towards real-time decision making, as the data is analyzed almost the moment it is captured.

What are the trade-offs?

Continuously streaming in new data and learning from it is demanding, and increases the risk of the model forming bad habits. 

How is it being used?

Online machine learning is ideal for systems where data changes rapidly, such as customer behavior.

What is it?


Online machine learning is a method for training models using a constant stream of real-time data. The model learns as each new individual data point is streamed in, continuously improving the quality and relevance of its outputs.



It’s especially useful in use cases where conditions constantly change. For example, if you trained a model to understand customer sentiment about your products using a fixed set of data, it wouldn’t be able to accurately infer meaning from discussions about new products, or from new topics of conversation among customers.


Online machine learning improves efficiency and adaptability for time-sensitive use cases like autonomous vehicles and medical monitoring, and lowers operational costs by making it easy to update models without re-training them from scratch.

What’s in for you?


Online machine learning can improve your ability to gain insights in near real time from customer data, enabling you to improve decision making. 



What’s more, online learning eliminates the need to re-train your model from scratch should you need to make a significant update. This means you can scale your models faster, saving you money and resources compared to traditional machine learning methods.

This makes online learning a sound choice if you expect the data used to train your machine learning models to change dramatically, or you’re using real-time data, such as customer behavior data. 


Online machine learning can also be highly beneficial to your customers and partners. For example, by analyzing real-time data, you can provide up-to-the-second insights and personalized customer experiences at scale.

What are the trade offs?


Online machine learning isn’t always simple. Due to its dynamic nature it can lead to potential complexities you’d be able to account for from day one by using fixed data.



This approach isn’t suitable for all use cases, and online methods can be difficult to maintain. For example, you may find that corrupt or unstable data is influencing your model and teaching it bad habits, which could lead to costly downtime and negative user experiences.


To ensure consistent high-quality models, you’ll need to constantly monitor the quality of your data, which may not work for businesses that don’t have the necessary tools or resources.

How is it being used?


Online machine learning is currently used for streaming analytics in the entertainment sector, enabling streaming platforms to provide personalized recommendations based on real-time customer behavior.



This same use case can be seen across all industries that rely on data analytics to provide personalized service experiences. For example, in retail, shopper behavior is often used to inform wish-lists, product recommendations, and advertisements.


Other common use cases include dynamic e-commerce pricing, personalized marketing content creation, fraud detection, and even AI-powered voice assistants — which mold their behavior as they learn more about user engagement.

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