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notebooks/official/custom/README.md

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+[Custom training and batch prediction](official/custom/sdk-custom-image-classification-batch.ipynb)
+
+Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- Upload the trained model artifacts as a `Model` resource.
+- Make a batch prediction.
+
+[Profile model training performance using Profiler](official/custom/custom_training_tensorboard_profiler.ipynb)
+
+Learn how to enable Vertex AI TensorBoard Profiler for custom training jobs.
+
+The steps performed include:
+
+- Setup a service account and a Cloud Storage bucket
+- Create a TensorBoard instance
+- Create and run a custom training job
+- View the TensorBoard Profiler dashboard
+
+
+[Training a TensorFlow model on BigQuery data](official/custom/custom-tabular-bq-managed-dataset.ipynb)
+
+Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then get a prediction from the deployed model by sending data.
+
+The steps performed include:
+
+- Create a Vertex AI custom `TrainingPipeline` for training a model.
+- Train a TensorFlow model.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model` resource.
+
+[Custom training and online prediction](official/custom/sdk-custom-image-classification-online.ipynb)
+
+Learn to use `Vertex AI Training` to create a custom-trained model from a Python script in a Docker container, and learn to use `Vertex AI Prediction` to do a prediction on the deployed model by sending data.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- Upload the trained model artifacts to a `Model` resource.
+- Create a serving `Endpoint` resource.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model` resource.
+
+[Deploying Iris-detection model using FastAPI and Vertex AI custom container serving](official/custom/SDK_Custom_Container_Prediction.ipynb)
+
+Learn how to create, deploy and serve a custom classification model on Vertex AI.
+
+The steps performed include:
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+- Train a model that uses flower's measurements as input to predict the class of iris.
+- Save the model and its serialized pre-processor.
+- Build a FastAPI server to handle predictions and health checks.
+- Build a custom container with model artifacts.
+- Upload and deploy custom container to Vertex AI Endpoints.