Online and batch learning are two common methods of training machine learning models. They differ in how they process the data and update the model parameters. In this article, you will learn the main features, advantages, and disadvantages of each method, and some examples of when to use them.
Online learning, also known as incremental or sequential learning, is a method that trains the model on one or a few data points at a time. The model updates its parameters after each data point or mini-batch, and discards the data after using it. Online learning is suitable for scenarios where the data is continuously generated, such as streaming data, sensor data, or user feedback. Online learning can adapt to changing data patterns and reduce memory requirements, but it can also be sensitive to noise and outliers, and require careful tuning of the learning rate.
- Before deciding to go with online learning, one has to consider how susceptible the system is to noise in real time. For example, suppose you are running online learning on a website to personalise the in-session experience. In that case, you must be confident in your website's ability to detect and block bot traffic. Otherwise, you risk optimising the web experience for bots and deteriorating it for real users.
Batch learning, also known as offline or batched learning, is a method that trains the model on the entire data set at once. The model updates its parameters after each iteration over the data set, and keeps the data for future use. Batch learning is suitable for scenarios where the data is fixed, stable, and large, such as historical data, census data, or scientific data. Batch learning can achieve higher accuracy and consistency, but it can also be computationally expensive, memory intensive, and slow to update.
- In my experience, most AI prototypes suffice using batch learning. The benefits of opting for batch learning usually involve faster time-to-market and less system maintenance by the engineers; these are crucial to any disruptive startups that want to create value fast and get their products out before any competitor might.
Hybrid learning, also known as mini-batch learning, is a method that combines online and batch learning. It trains the model on small batches of data, rather than single data points or the entire data set. The model updates its parameters after each batch, and may or may not keep the data. Hybrid learning is a compromise between online and batch learning that balances speed, accuracy, and memory. It is widely used in deep learning frameworks, such as TensorFlow and PyTorch, where the batch size is a hyperparameter that can be adjusted.
- Hybrid learning is unrealistic for most tech companies today, and 9/10 use cases are solvable using either batch or online learning infrastructure. The only scenarios that justify hybrid learning include ones where neither batch nor online learning can handle the complexity of the data. Additionally, hybrid learning excels in optimising objective functions that are out of bounds for either of the previous learning structures. The cons are that hybrid learning comes with increased system complexity, cost, and maintenance.
When choosing between online and batch learning, it's important to consider factors such as data availability, size, quality, model performance, and update. Online learning requires data to be available in real time or near real time and can handle large or infinite data sets. It's also adept at coping with noisy or dynamic data and can achieve fast and adaptive results. On the other hand, batch learning requires data to be available in advance or in batches and can handle small or medium data sets. It's better suited for clean or static data and can achieve accurate and consistent results. Additionally, online learning can update the model frequently and incrementally, while batch learning can update the model periodically and globally.
Let's consider startups. Online learning is often more common due to its advantages in scalability and adaptability, because of fast-changing environments where the ability to quickly adapt to new information is crucial. Online learning models can update on the fly, allowing real-time insights without recalculating models from scratch. This not only saves computational resources but also enables faster decision-making. The main downside of using online learning for a startup is the risk of less accurate models due to sensitivity to data noise and outliers.
To illustrate the difference between online and batch learning, here are some examples. Online learning is used in applications such as recommender systems, spam filtering, sentiment analysis, speech recognition, anomaly detection, and reinforcement learning. Batch learning is used for tasks such as image classification, natural language processing, clustering, regression, dimensionality reduction, and supervised learning.
Online and batch learning are not mutually exclusive, and you can use both methods in different stages of your machine learning project. For instance, batch learning can be used to train a model on a large and reliable data set, while online learning is better suited to fine-tune the model on new and changing data. When using online and batch learning, it's important to consider the data availability, size, quality, and distribution. Additionally, you should monitor and evaluate your model performance and update frequency as well as experiment with different learning rates, batch sizes, and regularization techniques. Finally, hybrid learning is an effective way to balance the trade-offs between online and batch learning.
Robustness to Concept Drift: Online Learning: Online learning is more robust to concept drift, which is the phenomenon where the data distribution changes over time. It can adapt to changing data patterns without retraining the entire model. Batch Learning: Batch learning is less robust to concept drift, as it typically assumes that the data distribution remains relatively stable during the training process.