This page lists known issues with AutoML Tables, along with ways you can avoid or recover from these issues.
High latency for online prediction requests after deploying the model
After you deploy your model, the first online prediction requests show increased latency.
To avoid this issue, send a few prediction requests to warm up the model before using it in production.
Batch prediction requests with only one feature column fail.
You must provide at least 2 feature columns with batch predictions.
Using Google Cloud Platform Console with AutoML Tables
User experience with Microsoft Edge and Microsoft Internet Explorer browsers might be suboptimal.
Microsoft Edge and Microsoft Internet Explorer do not support all features of AutoML Tables. If you are having problems, try Google Chrome, Safari, or Firefox.
The following issues were listed on this page, but are no longer affecting AutoML Tables.
Training for longer than needed no longer degrades model quality.
AutoML Tables automatically stops training the model when it detects that model quality is no longer improving.
Datasets with less than 100,000 rows no longer result in decreased model quality.
Datasets with less than 100,000 rows can be used to train models without a significant drop in model quality. Keep in mind that more data typically results in better model quality. The minimum amount of training data is 1,000 rows.