[Get started with BigQuery datasets](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/datasets/get_started_bq_datasets.ipynb)

```
Learn how to use `BigQuery` as a dataset for training with `Vertex AI`.

The steps performed include:

- Create a Vertex AI `Dataset` resource from `BigQuery` table -- compatible for `AutoML` training.
- Extract a copy of the dataset from `BigQuery` to a CSV file in Cloud Storage -- compatible for `AutoML` or custom training.
- Select rows from a `BigQuery` dataset into a `pandas` dataframe -- compatible for custom training.
- Select rows from a `BigQuery` dataset into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.
- Select rows from extracted CSV files into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.
- Create a `BigQuery` dataset from CSV files.
- Extract data from `BigQuery` table into a `DMatrix` -- compatible for custom training `XGBoost` models.

```

   Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro).

   Learn more about [Vertex AI for BigQuery users](https://cloud.google.com/vertex-ai/docs/beginner/bqml).


[Get started with Vertex AI Data Labeling](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/datasets/get_started_with_data_labeling.ipynb)

```
Learn how to use the `Vertex AI Data Labeling` service.

The steps performed include:

- Create a Specialist Pool for data labelers.
- Create a data labeling job.
- Submit the data labeling job.
- List data labeling jobs.
- Cancel a data labeling job.

```

   Learn more about [Vertex AI Data Labeling](https://cloud.google.com/vertex-ai/docs/datasets/data-labeling-job).