Meta-Learning
Definition
Meta-learning uses multiple tasks to learn adaptation to new tasks with few data or steps.
Methods that learn across multiple tasks how models can adapt with little data or few steps.
Simply explained
How to develop a better learning strategy across many types of practice; a new task must still be suitable.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
A model adapts to a new error type with few examples.
Limits and pitfalls
- Success with few examples requires suitable tasks. Overlaps between learning across tasks and final testing can bias results.
Origin & history
Meta-learning has roots in learning psychology and ML; MAML from 2017 is a well-known gradient-based approach.
Use in mid-sized companies
- Possible example: A development team separates learning and test tasks before investigating rapid adaptation to new error types. They examine task similarity and quality with few examples.
Frequently asked questions
What does meta-learning mean?
Methods that learn across multiple tasks how models can adapt with little data or few steps.
Which mistakes should be avoided?
Do not expect few-shot success without task similarity or clean meta-test separation.
Sources
Source checked on 10.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
Editorially reviewed on: