Online Machine Learning
Online machine learning is a dynamic approach where the model is trained and updated continuously as each new piece of data becomes available. This contrasts with traditional batch learning, where models are trained on a complete dataset in a single or multiple iterations.
Online learning is particularly useful in situations where the data stream is continuous and vast, making it impractical to store and retrain from scratch with each new data point.
It's also beneficial when the data's distribution changes over time—a phenomenon known as concept drift. In online learning, algorithms adjust the model incrementally, often with each new data point, optimizing computational resources and enabling real-time learning and adaptation.
A practical application of online machine learning is in financial markets, where algorithms continuously update their models to predict stock prices based on incoming data. Each new piece of market data—such as stock prices, trading volume, and economic indicators—can be used to update the model's predictions in real-time.
Another example is in recommendation systems, such as those used by streaming services, where the system must adapt to each user's changing preferences and the introduction of new content.
Online learning algorithms can update user profiles and recommendation models as new viewing data becomes available, ensuring that recommendations remain relevant and personalized.