How do you deal with the issue of scalability when training on large datasets?

Scalability is an important issue when training on large datasets as traditional machine learning techniques may not be effective in handling such large amounts of data. Here are some approaches that can help:

• Use distributed algorithms: Rather than training on a single machine, you can use a cluster of machines which can distribute the computational load.
• Use smaller sets of features: Selecting a subset of features from the dataset can reduce the dimensionality of the dataset without losing too much information.
• Use online training: Online training allows the model to be continuously updated with new data and can handle large datasets more efficiently.
• Use data partitioning: Partitioning the dataset into smaller subsets and training the model on each subset can simplify the problem and make it more manageable.

It is important to experiment with different methods and evaluate their performance.