diff --git a/README.md b/README.md
index 163ca5655..71362b023 100644
--- a/README.md
+++ b/README.md
@@ -55,7 +55,7 @@ Use the following guides to get started with FLAML in .NET:
 
 - [Install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022)
 - [Install ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows)
-- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0-preview.22313.1)
+- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0)
 
 ## Quickstart
 
@@ -107,7 +107,7 @@ In addition, you can find:
 
 - Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute).
 
-- ML.NET documentation and tutorials for [Model Builder](https://docs.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://github.com/dotnet/csharp-notebooks/blob/main/machine-learning/03-Training%20and%20AutoML.ipynb).
+- ML.NET documentation and tutorials for [Model Builder](https://learn.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://learn.microsoft.com/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://learn.microsoft.com/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api).
 
 ## Contributing
 
diff --git a/website/docs/FAQ.md b/website/docs/FAQ.md
index 2fdbcd2fd..232e390c0 100644
--- a/website/docs/FAQ.md
+++ b/website/docs/FAQ.md
@@ -66,3 +66,16 @@ Packages such as `azureml-interpret` and `sklearn.inspection.permutation_importa
 Model explanation is frequently asked and adding a native support may be a good feature. Suggestions/contributions are welcome.
 
 Optimization history can be checked from the [log](Use-Cases/Task-Oriented-AutoML#log-the-trials). You can also [retrieve the log and plot the learning curve](Use-Cases/Task-Oriented-AutoML#plot-learning-curve).
+
+
+### How to resolve out-of-memory error in `AutoML.fit()`
+
+* Set `free_mem_ratio` a float between 0 and 1. For example, 0.2 means try to keep free memory above 20% of total memory. Training may be early stopped for memory consumption reason when this is set.
+* Set `model_history` False.
+* If your data are already preprocessed, set `skip_transform` False. If you can preprocess the data before the fit starts, this setting can save memory needed for preprocessing in `fit`.
+* If the OOM error only happens for some particular trials:
+    - set `use_ray` True. This will increase the overhead per trial but can keep the AutoML process running when a single trial fails due to OOM error.
+    - provide a more accurate [`size`](reference/automl/model#size) function for the memory bytes consumption of each config for the estimator causing this error.
+    - modify the [search space](Use-Cases/Task-Oriented-AutoML#a-shortcut-to-override-the-search-space) for the estimators causing this error.
+    - or remove this estimator from the `estimator_list`.
+* If the OOM error happens when ensembling, consider disabling ensemble, or use a cheaper ensemble option. ([Example](Use-Cases/Task-Oriented-AutoML#ensemble)).