Real-time Machine Learning: Online Learning with PubNub

The article explores the concept and application of online learning, a machine learning approach that processes data in real-time by incrementally updating models, which is particularly advantageous for dynamic datasets such as IoT data streams. It contrasts traditional machine learning, which requires fitting entire datasets at once, with online learning's ability to handle data in subsets, allowing for faster deployment, frequent updates, and reduced memory usage. The article focuses on implementing Stochastic Gradient Descent (SGD) as an online learning method, which efficiently approximates the global minimum with fewer iterations than standard gradient descent. It details building a real-time learning model using Python and PubNub's Data Stream Network to facilitate continuous data consumption and model updating. The process involves data pre-processing, streaming, and model training, culminating in a system where the model's accuracy progressively improves as it adapts to new data chunks, achieving a validation mean accuracy of around 70% on a synthetic dataset.