Glossary term
Glossary term
Training and Fine-Tuning
Any automated process for building machine learning models. AutoML can automatically do tasks such as the following:
Search for the most appropriate model.
Tune hyperparameters.
Prepare data (including performing feature engineering).
Deploy the resulting model.
AutoML is useful for data scientists because it can save them time and effort in developing machine learning pipelines and improve prediction accuracy. It is also useful to non-experts, by making complicated machine learning tasks more accessible to them.
See Automated Machine Learning (AutoML) in Machine Learning Crash Course for more information.
Created for this library
A marketing analytics team uses AutoML to spin up a churn baseline in a single afternoon so it can focus engineering effort on the high-value campaigns.
A mid-size manufacturer adopts AutoML so its lone data scientist can stand up demand forecasts across 200 SKUs without writing custom pipelines.
A small e-commerce company relies on AutoML for its product categorization model so an analyst can retrain it when the catalog grows.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License