Google Vertex AI

Overview

Google Vertex AI is a managed machine learning platform within Google Cloud that consolidates tools for building, deploying, and scaling ML models. Announced in 2021, it aims to streamline the MLOps process by offering a unified interface for various stages of the ML lifecycle, from data ingestion and preparation to model training, evaluation, deployment, and monitoring Google Vertex AI introduction. The platform is designed for both data scientists and ML engineers, providing options for low-code development, custom model training, and integration with pre-trained models.

Vertex AI is positioned for enterprises that require a comprehensive solution for managing large-scale data science projects and operationalizing ML models. It supports a range of machine learning tasks, including tabular data, image, video, and text processing. Key components include managed datasets, feature stores, model training environments, prediction endpoints, and MLOps tools like pipelines and model monitoring. The platform also incorporates generative AI capabilities through services like Generative AI Studio, allowing developers to access and fine-tune large language models (LLMs) and other foundation models Vertex AI Generative AI overview.

Developers can interact with Vertex AI through its console, client libraries (Python, Java, Node.js, Go), and REST APIs. The Python SDK is frequently used for scripting ML workflows and interacting with services like Vertex AI Training and Prediction. The platform's integration with other Google Cloud services, such as Cloud Storage, BigQuery, and Dataflow, facilitates data management and processing for ML workloads. For example, data scientists can use Vertex AI Workbench for notebook-based development and connect directly to BigQuery for data access.

Vertex AI's focus on enterprise-grade MLOps includes features like experiment tracking, model versioning, and continuous integration/continuous delivery (CI/CD) for ML. This enables teams to manage the lifecycle of models from development to production, ensuring reproducibility and governance. The platform offers various compliance certifications, including SOC 1, SOC 2, SOC 3, ISO 27001, ISO 27017, ISO 27018, PCI DSS, HIPAA, and GDPR, addressing common enterprise requirements for data security and privacy Google Cloud compliance details. For comparison, a competing platform like Amazon SageMaker also emphasizes end-to-end ML lifecycle management, providing similar tools for data preparation, training, and deployment, often catering to organizations standardized on AWS infrastructure Amazon SageMaker features.

Key features

• Model Garden: A catalog of pre-trained models, including foundation models, and components for fine-tuning and deployment.
• Vertex AI Workbench: Managed Jupyter notebooks integrated with Google Cloud services, facilitating interactive development and experimentation.
• Vertex AI Training: Tools for distributed model training, supporting custom containers, built-in algorithms, and hyperparameter tuning.
• Vertex AI Prediction: Managed endpoints for deploying ML models for online and batch predictions, with automatic scaling and versioning.
• Vertex AI Feature Store: A centralized repository for managing, serving, and sharing ML features consistently across models.
• Vertex AI Pipelines: Orchestration service for automating and managing ML workflows, enabling reproducible and scalable MLOps.
• Generative AI Studio: A platform for experimenting with, customizing, and deploying Google's generative AI models, including LLMs and diffusion models.
• Vertex AI Search: Enterprise search capabilities powered by Google's AI, enabling semantic search over unstructured data.
• Vertex AI Conversation: Tools for building conversational AI agents, including chatbots and virtual assistants.

Pricing

Vertex AI pricing operates on a pay-as-you-go model, based on the consumption of underlying Google Cloud resources and specific Vertex AI services. This includes charges for compute resources (e.g., virtual machines for training and prediction), storage (for datasets and models), and specialized features like Vertex AI Feature Store, Pipelines, and Generative AI Studio model usage.

As of 2026-06-09, detailed pricing information, including free tier limits for certain services, is available on the official Google Vertex AI pricing page.

Common integrations

• Google Cloud Storage: For storing datasets, model artifacts, and pipeline outputs Google Cloud Storage documentation.
• BigQuery: For data warehousing and analytics, often used as a source for ML datasets BigQuery documentation.
• Dataflow: For large-scale data processing and transformation, often used in conjunction with ML pipelines Google Cloud Dataflow reference.
• Cloud Logging and Monitoring: For observing model performance, resource utilization, and debugging ML workflows Cloud Logging documentation.
• Cloud Identity and Access Management (IAM): For managing permissions and access control to Vertex AI resources Cloud IAM overview.
• TensorFlow and PyTorch: Direct integration with popular ML frameworks for model development and training TensorFlow website.

Alternatives

• Amazon SageMaker: A comprehensive ML service from AWS that covers the entire ML lifecycle with various tools for data scientists and developers.
• Microsoft Azure Machine Learning: Microsoft's cloud-based platform for building, training, and deploying ML models, offering MLOps capabilities and integration with Azure services.
• Databricks Lakehouse Platform: Focuses on unifying data, analytics, and AI workloads on a single platform, often used for large-scale data science and ML engineering.

Getting started

The following Python code snippet demonstrates how to initialize the Vertex AI SDK, upload a dataset, and train a simple tabular classification model using AutoML Tables. This example assumes you have authenticated with Google Cloud and set your project ID.

from google.cloud import aiplatform

# --- Configuration Variables ---
PROJECT_ID = "your-gcp-project-id"
REGION = "us-central1"
DATASET_DISPLAY_NAME = "my-automl-dataset"
TRAINING_JOB_DISPLAY_NAME = "my-automl-training-job"
MODEL_DISPLAY_NAME = "my-automl-model"

# Initialize Vertex AI SDK
aiplatform.init(project=PROJECT_ID, location=REGION)

# --- Step 1: Create or get a Dataset ---
# For this example, we'll assume you have a CSV file in GCS
# Replace with your GCS path to a CSV file (e.g., gs://cloud-samples-data/ai-platform/iris/iris.csv)
GCS_SOURCE_URI = "gs://your-bucket/your-data.csv"

try:
    # Attempt to retrieve an existing dataset
    datasets = aiplatform.TabularDataset.list(
        filter=f'display_name="{DATASET_DISPLAY_NAME}"', order_by="create_time desc"
    )
    if datasets:
        dataset = datasets[0]
        print(f"Using existing dataset: {dataset.resource_name}")
    else:
        # Create a new dataset if it doesn't exist
        print(f"Creating new dataset: {DATASET_DISPLAY_NAME}")
        dataset = aiplatform.TabularDataset.create(
            display_name=DATASET_DISPLAY_NAME,
            gcs_source=[GCS_SOURCE_URI],
            sync=True
        )
        print(f"Dataset created: {dataset.resource_name}")
except Exception as e:
    print(f"Error creating/getting dataset: {e}")
    # Handle specific errors or exit
    exit()

# --- Step 2: Configure and run an AutoML Training Job ---
# Define the target column in your dataset
TARGET_COLUMN = "target_column_name"

# Create an AutoML Tabular Training Job
job = aiplatform.AutoMlTablesTrainingJob(
    display_name=TRAINING_JOB_DISPLAY_NAME,
    optimization_prediction_type="classification", # or "regression"
    column_transformations=[
        {"column_name": TARGET_COLUMN, "auto": {}}
        # Add other column transformations as needed
    ]
)

# Run the training job
print(f"Starting training job: {TRAINING_JOB_DISPLAY_NAME}")
model = job.run(
    dataset=dataset,
    target_column=TARGET_COLUMN,
    model_display_name=MODEL_DISPLAY_NAME,
    training_fraction_split=0.8, # 80% for training
    validation_fraction_split=0.1, # 10% for validation
    test_fraction_split=0.1, # 10% for testing
    budget_milli_node_hours=1000, # 1 hour budget
    sync=True
)

print(f"Training job completed. Model: {model.resource_name}")

# --- Step 3: Deploy the Model to an Endpoint (Optional) ---
# To deploy, you would typically create an Endpoint and then deploy the model to it.
# This part is commented out for brevity but shows the next logical step.
# endpoint = aiplatform.Endpoint.create(
#     display_name=f"{MODEL_DISPLAY_NAME}-endpoint"
# )
# model.deploy(endpoint=endpoint, deployed_model_display_name=f"{MODEL_DISPLAY_NAME}-deployed")
# print(f"Model deployed to endpoint: {endpoint.resource_name}")