1. Introduction
In this tutorial, we’ll review online and offline Learning: what they are, how they work, and how they differ.
2. Definitions
Machine learning is all about teaching machines how to learn on their own without explicit instructions. This involves the application of various learning algorithms to data to facilitate the learning process. When we talk of learning in machine learning, we are referring to how the machines acquire knowledge on how to perform tasks.
When it comes to learning in machine learning, two general themes exist: supervised and unsupervised learning. Supervised and unsupervised learning can further be facilitated by two approaches: online and offline learning. Let’s dive into the details of these in the next sections.
3. Online Learning
When we talk of online learning, we refer to instances where learning occurs as the data becomes available. Alternatively, we also mean learning by considering one observation at a time. In this case, the model parameters get updated each time it receives a new observation.
In online learning, we train the model over observation, update the parameters, and iterate over these till we obtain a model that can be used for the task at hand.
This process of constantly learning through updating the parameters makes online machine learning adaptable to different types of data.
For example, let’s suppose we want to train a model to recognize weather patterns. We can train the model to work on temperature readings taken at different times in order to determine the weather patterns. In this case, the model we train is able to learn the temperature readings of different weather patterns on the fly:
3.1. Advantages and Disadvantages
One major benefit to online learning is adaptability. The model is able to adjust and learn from data with different patterns and distributions as they come. Most importantly, online learning does not require so much memory for storing data. Once the model has been trained over a specific observation, there is no need to store it.
A drawback to online learning is the complexity behind developing or implementing it. Because learning takes place on the fly, we have to consider how the model will be updated and how the data will be processed just to name a few. Ultimately this requires more resources hence capital intensive.
3.2. Applications
The adaptability of online learning makes it suitable for real-time tasks. Some notable applications of online learning are:
- Streaming Analytics – analyzing data in real-time from sensors and other IoT devices
- Weather forecasting
- Stock price prediction
4. Offline Learning
Simply put, offline or batch learning refers to learning over all the observations in a dataset at a go. We can also say that models in offline learning learn over a static dataset. We collect data and then train a machine learning model to learn from this data.
In our previous example of learning weather patterns. For offline learning, we collect the weather readings for six months and then train a model over this data collection.
Additionally, in offline learning, the parameters of the machine learning model are updated when learning has been completed over the entire dataset:
4.1. Advantages and Disadvantages
Offline learning is preferred for its simplicity over online learning. Implementing an offline learning model is straightforward as it does not require extra computational capabilities for real-time processing.
However, offline learning is not as adaptable to different patterns in data as compared to online learning. This means that any improvements to the model will require retraining over the entire dataset. In addition, storage space is usually required to keep the entire dataset.
4.2. Applications
Some notable applications of online learning are:
- Image recognition tasks
- Classification tasks
5. Differences and Similarities
The differences and similarities between online and offline learning are mainly in the way learning is done. Let’s look at these:
Online Learning
Offline Learning
Learning is done incrementally on the dataset
Learning is done once on the dataset
Model is adaptable to different data
Model is not adaptable
Complex to develop
Less complex to develop
Requires more computations
Fewer computations required
Less storage space is required
Requires storage to store the entire dataset
It can be expensive as it is resource intensive
Less expensive
6. Conclusion
In this tutorial, we reviewed online and offline learning. Online learning considers single observations of data during training, whereas offline learning considers all the data at one time during training. Offline learning is easier to implement compared to online learning.
In summary, the choice of which learning mode to adopt is based on the machine learning algorithms in use and the task at hand.