Vertex AI for Data Engineering

Master Vertex AI including Feature Store, Pipelines, data labeling, and ML integration patterns for data engineers.

18 min readAdvanced

Vertex AI Architecture

🏗️ GCP Data Engineering Reference Architecture

Interview Tip: GCP's data engineering stack is serverless-first. Dataflow (Apache Beam) handles both streaming and batch. BigQuery is the flagship analytics service.

Feature Store

from google.cloud import aiplatform

# Initialize Vertex AI
aiplatform.init(project='my-project', location='us-central1')

# Create Feature Store
featurestore = aiplatform.featurestore.Featurestore.create(
    featurestore_id='user_features',
    online_store_fixed_node_count=10
)

# Create entity type (table)
entity_type = featurestore.create_entity_type(
    entity_type_id='users',
    description='User features'
)

# Create features (columns)
entity_type.create_feature(
    feature_id='age',
    value_type=aiplatform.featurestore.ValueType.INT64,
    description='User age'
)

entity_type.create_feature(
    feature_id='segment',
    value_type=aiplatform.featurestore.ValueType.STRING,
    description='User segment'
)

# Ingest data from BigQuery
entity_type.ingest_from_bq(
    feature_ids=['age', 'segment'],
    bq_source_uri='bq://project.dataset.users',
    entity_id_field='user_id'
)

# Serve features for prediction
features = entity_type.read(
    entity_ids=['user_123', 'user_456'],
    feature_ids=['age', 'segment']
)

ML Pipelines

from google_cloud_pipeline_components.v1 import BigQueryQueryJobOp
from kfp import dsl

@dsl.pipeline(
    name='feature-engineering-pipeline',
    pipeline_root='gs://my-pipeline-root/'
)
def feature_pipeline():
    # Step 1: Extract features from BigQuery
    extract = BigQueryQueryJobOp(
        query='SELECT * FROM `project.dataset.raw_features`',
        project='my-project',
        location='us-central1'
    )

    # Step 2: Transform features
    transform = dsl.ContainerOp(
        name='transform-features',
        image='gcr.io/my-project/feature-transformer:latest',
        arguments={
            'input': extract.outputs['job'],
        }
    )

    # Step 3: Write to Feature Store
    load = dsl.ContainerOp(
        name='load-feature-store',
        image='gcr.io/my-project/feature-loader:latest',
        arguments={
            'input': transform.outputs['output'],
        }
    )

✨

Best Practice: Use Feature Store for centralized feature management. Implement ML Pipelines for reproducible workflows. Use Vertex AI Workbench for development. Version all models and features. Monitor model performance with Model Monitoring.

💬

Common Interview Questions

Q1: What is Vertex AI Feature Store?

Answer: Feature Store is a centralized repository for storing, serving, and managing ML features. It provides online and offline feature serving, feature versioning, and integration with training and prediction pipelines.

Q2: What is the difference between AutoML and custom training?

Answer: AutoML automates model selection, hyperparameter tuning, and training. Custom training gives full control over algorithms, frameworks, and infrastructure. Use AutoML for standard tasks; custom for specialized requirements.

Q3: How do ML Pipelines help data engineers?

Answer: ML Pipelines orchestrate ML workflows including data preparation, feature engineering, training, and deployment. They provide reproducibility, versioning, and automation. Data engineers build the data pipelines that feed ML pipelines.

Q4: What is the benefit of Feature Store for production?

Answer: Feature Store provides consistent feature computation for training and serving, reducing training-serving skew. It offers low-latency online serving and scalable offline access for batch prediction.

Q5: How do you integrate Vertex AI with BigQuery?

Answer: 1) Use BigQuery for training data, 2) Use Feature Store for feature serving, 3) Use BigQuery ML for model training, 4) Use Vertex AI for advanced ML, 5) Write predictions back to BigQuery.

Vertex AI: Feature Store, Pipelines & Data Labeling