Vertex AI Platform
AIEnterprise-ready, fully-managed, unified AI development platform on Google Cloud for building, deploying, and managing ML and generative AI.
-(0 Reviews)
Paid
See pricing details
Vertex AI Platform
AI
Enterprise-ready, fully-managed, unified AI development platform on Google Cloud for building, deploying, and managing ML and generative AI.
-(0 Reviews)
Paid
See pricing details
About Vertex AI Platform
Vertex AI is Google Cloud's fully managed, end-to-end platform for developing, training, deploying, and monitoring machine learning and generative AI models. It unifies AutoML and custom model workflows, provides Vertex AI Studio and Agent Builder for rapid prototype-to-production development, and offers access to a Model Garden of first-party and third-party foundation models. Vertex AI integrates with Google Cloud data services (BigQuery, Feature Store, Cloud Storage), provides managed training and serving infrastructure, tooling for MLOps (pipelines, model registry, monitoring), and SDKs for programmatic control, enabling enterprises to scale AI workloads with cloud-native security and governance.
Key Features
Vertex AI Studio: A web-based IDE and visual interface for building, experimenting, and managing datasets, notebooks, pipelines, and model deployments to accelerate end-to-end ML workflows.
Model Garden & Foundation Models: Access to 160+ first-party and third-party foundation models (including Gemini, Gemma, Llama 3, Claude 3) and curated model registry for easy discovery and deployment of pretrained models.
Managed Training & Serving: Fully-managed training infrastructure with custom training containers, AutoML options, hyperparameter tuning, and autoscaling prediction endpoints for online and batch inference.
Agent Builder & Generative Tools: Tooling to construct generative AI agents, orchestrate multimodal pipelines, and integrate large models into conversational or task-oriented agents for production.
Feature Store & Data Integration: Built-in Feature Store and native integrations with BigQuery, Cloud Storage, and data labeling to streamline dataset management and feature engineering at scale.
MLOps & Monitoring: Model Registry, Vertex Pipelines, continuous evaluation, model monitoring, explainability and drift detection to operationalize models with governance and reproducibility.
SDKs & Extensibility: Official client libraries (google-cloud-aiplatform), samples and GitHub repositories to programmatically build, upload, deploy, and monitor models, plus support for custom prediction containers and third-party hubs (e.g., Hugging Face).
Security & Enterprise Controls: Enterprise-grade IAM integration, VPC Service Controls, and compliance features to manage access, isolation, and secure deployment of models in production.
Fully-managed end-to-end ML platform (development, training, deployment, monitoring)
Vertex AI Studio and Agent Builder for building workflows and agents
Model Garden with first-party, open-source and third-party foundation models (e.g., Gemini, Gemma, Llama 3, Claude 3)
Python SDK (google-cloud-aiplatform) with GAPIC-backed client libraries
AutoML and custom model training workflows
Model Registry to create, register and manage model versions
Feature Store for online serving and integration with BigQuery and Data Labeling
Prebuilt containers and support for custom prediction containers (serving_container_image_uri)
Online prediction endpoints and scalable serving with configurable machine types
Integrations with Hugging Face Hub (deploy Hub models), Ray clusters, Colab Enterprise, Cloud Run, Cloud SQL and BigQuery
Support for custom prediction code via custom Predictor and CPR images
Underlying REST/gRPC APIs auto-generated from Google service proto files (GAPIC)
Use Cases
Deploying pretrained or custom models to production: Create model resources, register them in the Model Garden/Registry, and deploy autoscaling endpoints for low-latency online prediction or batch inference.
Running Hugging Face or third-party models: Upload artifacts or use containers to deploy models from external hubs (e.g., Hugging Face) via the Vertex AI SDK and serve them as managed endpoints.
Building generative AI assistants and agents: Use Agent Builder and foundation models to construct conversational agents that connect to data sources, tools, and cloud services for task automation.
End-to-end MLOps pipelines: Orchestrate data preparation, training, evaluation, and deployment with Vertex Pipelines and the Feature Store to ensure reproducible CI/CD for ML.
Scalable training and hyperparameter tuning: Execute distributed training jobs, custom containers, and automated hyperparameter searches on managed compute to accelerate model development.
Integrating ML with data warehouses: Connect BigQuery datasets and Cloud Storage to train models directly on enterprise data and serve predictions integrated with analytics workflows.
Deploying Hugging Face models for online inference as Vertex AI endpoints
Building generative AI agents and applications using Agent Builder and foundation models
Productionizing ML pipelines with Model Registry, Feature Store and MLOps practices
Training AutoML models and custom models with prebuilt containers and custom training jobs
Running large-scale inference and LLMOps workflows integrated with BigQuery and Cloud services
Screenshots
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