AI Notes

Google BigQuery and Vertex AI Together

Google BigQuery and are powerful components of Google Cloud’s /ML ecosystem and are designed to work seamlessly together to facilitate the entire machine learning lifecycle. Here’s how they integrate and how you can leverage them together:

Key Integration Points and Use Cases:

  1. Data Preparation and Feature Engineering (BigQuery to Vertex AI):
    • Data Storage: BigQuery serves as an excellent data warehouse to store and manage the large datasets needed for training ML models in Vertex AI.
    • Data Exploration and Analysis: You can use BigQuery’s SQL capabilities to explore, clean, and analyze your data before training.
    • Feature Engineering: Perform feature engineering directly within BigQuery using SQL or User-Defined Functions (UDFs). This allows you to create the features needed for your models at scale.
    • Exporting Data for Training: Easily query and export prepared feature data from BigQuery to Cloud Storage, which Vertex AI can then access for training. Vertex AI’s managed datasets can directly connect to BigQuery tables.
  2. Model Training (Vertex AI using BigQuery Data):
    • Managed Datasets: Vertex AI allows you to create managed datasets directly from BigQuery tables. This simplifies the process of accessing and using your BigQuery data for training AutoML models or custom-trained models.
    • AutoML Training: Train AutoML models (for tabular data, images, text, video) directly on BigQuery tables without writing any training code. Vertex AI handles data splitting, model selection, hyperparameter tuning, and evaluation.
    • Custom Training: When using custom training jobs in Vertex AI (with TensorFlow, PyTorch, scikit-learn, etc.), you can configure your training script to read data directly from BigQuery using the BigQuery client library or by staging data in Cloud Storage first.
  3. Feature Store (Vertex AI Feature Store with BigQuery):
    • Centralized Feature Management: Vertex AI Feature Store can use BigQuery as its online and offline storage backend. This allows you to:
      • Store and serve features consistently for both training and online/batch inference.
      • Manage feature metadata and track feature lineage.
      • Easily access features prepared in BigQuery for model training in Vertex AI.
  4. Model Deployment and Prediction (Vertex AI using Models Trained on BigQuery Data):
    • Deploy Models: Once you’ve trained a model in Vertex AI (whether using AutoML or custom training with BigQuery data), you can deploy it to Vertex AI Endpoints for online or batch predictions.
    • Batch Prediction: Vertex AI Batch Prediction jobs can read input data directly from BigQuery tables and write predictions back to BigQuery tables, making it easy to process large volumes of data.
    • Online Prediction: For real-time predictions, your deployed Vertex AI Endpoint can receive prediction requests. The features used for these predictions can be retrieved from Vertex AI Feature Store (which might be backed by BigQuery).
  5. MLOps and Monitoring:
    • Data Monitoring: You can use BigQuery to analyze logs and monitoring data from your deployed Vertex AI models to track performance, detect drift, and troubleshoot issues.
    • Pipeline Orchestration (Vertex AI Pipelines): Vertex AI Pipelines can include steps that interact with BigQuery (e.g., data extraction, feature engineering) and steps that involve model training and deployment in Vertex AI.

Example Workflow:

  1. Store raw data in BigQuery.
  2. Use BigQuery SQL to explore, clean, and engineer features.
  3. Create a Vertex AI Managed Dataset directly from the BigQuery table.
  4. Train an AutoML Tabular model in Vertex AI using the Managed Dataset.
  5. Deploy the trained model to a Vertex AI Endpoint.
  6. For batch predictions, provide input data as a BigQuery table and configure the Vertex AI Batch Prediction job to write results back to BigQuery.
  7. Monitor model performance using logs stored and analyzed in BigQuery.

Code Snippet (Conceptual – Python with Vertex AI SDK and BigQuery Client):

Python

from google.cloud import bigquery
from google.cloud import aiplatform

# Initialize BigQuery client
bq_client = bigquery.Client(location="US")  # Adjust location as needed

# Initialize Vertex AI client
aiplatform.init(location="us-central1")  # Adjust location as needed

# --- Data Preparation in BigQuery ---
query = """
SELECT
    feature1,
    feature2,
    target
FROM
    your_project.your_dataset.your_table
WHERE
    split = 'train'
"""
train_table = bq_client.query(query).result().to_dataframe()

# --- Upload data to GCS (if not using Managed Datasets) ---
# from google.cloud import storage
# gcs_client = storage.Client()
# bucket = gcs_client.bucket("your-gcs-bucket")
# blob = bucket.blob("training_data.csv")
# blob.upload_from_string(train_table.to_csv(index=False), "text/csv")
# train_uri = "gs://your-gcs-bucket/training_data.csv"

# --- Create Vertex AI Managed Dataset from BigQuery ---
dataset = aiplatform.TabularDataset.create(
    display_name="your_dataset_name",
    bq_source="bq://your_project.your_dataset.your_table",
)

# --- Train AutoML Tabular Model ---
job = aiplatform.AutoMLTabularTrainingJob(
    display_name="automl_model_training",
    objective_column="target",
)
model = job.run(
    dataset=dataset,
    target_column="target",
    # ... other training parameters
)

# --- Deploy the Model to an Endpoint ---
endpoint = aiplatform.Endpoint.create(
    display_name="your_endpoint_name",
)
deployed_model = endpoint.deploy(model=model)

# --- Get Batch Predictions from BigQuery Data ---
batch_prediction_job = aiplatform.BatchPredictionJob.create(
    display_name="batch_prediction_job",
    model=deployed_model,
    bigquery_source="bq://your_project.your_dataset.prediction_input_table",
    bigquery_destination="bq://your_project.your_dataset.prediction_output_table",
)

print(f"Batch Prediction Job: {batch_prediction_job.name}")

In essence, BigQuery provides the scalable and efficient data foundation for your ML workflows in Vertex AI, while Vertex AI offers the tools and services for building, training, deploying, and managing your models. Their tight integration streamlines the entire process and allows you to leverage the strengths of both platforms.

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