End-to-end (E2E) learning definition
End-to-end (E2E) learning is a machine learning technique where a model is trained to learn the entire task, from input to final output, skipping the intermediate steps. The goal is to teach the model to recognize patterns, focus on what’s important, and make decisions all at once.
See also: training data, machine learning, data augmentation, zero-shot learning (ZSL), LLM temperature
Benefits of end-to-end (E2E) learning
- E2E learning simplifies things. You don’t have to manually break down the task or pick features — everything happens in one go.
- Since the model learns directly from the data during E2E learning, it often finds better patterns and makes more accurate predictions.
- Since the model learns to handle everything, you’re less likely to mess up a task with human decisions along the way.
- With fewer steps to manage, you can get your model up and running faster.
- Since the model learns to see the whole picture, the model tends to do better when faced with new data.
End-to-end (E2E) learning challenges
- The model has to learn everything on its own, so it requires a ton of data to do a good job.
- If something goes wrong, it’s tough to figure out exactly where the problem is since there are no clear intermediate steps.
- Training a model end-to-end can take a lot of computational power and time.
- If the model has too much data or complexity, it might learn to perform well on training data but struggle with new data.
- Since the model is doing everything, you might not have as much control over how it learns or what it focuses on.
