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Batch Machine Learning

In machine learning, batch learning also known as offline learning is a technique where the model is trained on the entire dataset available in certain time. Batch learning is suitable for scenarios where the data is fixed, stable, and large, such as historical data, census data. The model is trained in batches, where each batch is a subset of the entire dataset. The model’s weights are updated after analyzing each batch. Once the model has been trained on the entire dataset, the learning process ends. Batch learning can achieve higher accuracy and consistency, but it can also be computationally expensive.
In batch learning, the system is not capable of learning incrementally. The models must be trained using all available data every single time. The models then get trained with the accumulated data from time to time at periodic intervals. After the models are trained, they are launched into production and they run without learning anymore.

Improving the model performance would require re-training all over again with the entire training dataset. These models are static in nature which means that once they get trained, their performance will not improve until a new model gets create. The model’s performance tends to decay slowly over time, simply because the real world data continues to evolve while the model remains unchanged. This is often called “model rot” or “data drift”. The solution is to regularly retrain the model on up to date dataset.
For the model to learn new data, the model would need to be trained with all the data from scratch and old model need to be replaced with the new model. Model training using the full dataset can take many hours. Thus, it is recommended to run the batch frequently rather than whole dataset.

There can be various reasons why we can choose to adopt batch learning. Some of these reasons are the following

• The business requirements do not require frequent learning of model.
• Dataset distribution is not expected to change frequently.
• The expertise required for creating the system for incremental learning is rarely available

The criteria based on which the machine learning models can be decided to train in a batch manner depends on the model performance.

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