Vertex AI is Google Cloud's unified machine learning and artificial intelligence platform that provides tools and infrastructure for building, deploying, and scaling ML models and AI applications. Launched in 2021 as the successor to Google's AI Platform, Vertex AI consolidates Google Cloud's ML services into a single, integrated environment. It supports the full machine learning lifecycle, from data preparation and model training to deployment, monitoring, and management, catering to both data scientists and ML engineers.
One of Vertex AI's most significant offerings is access to Google's foundation models through the Model Garden, including the Gemini family of large language models. Developers can use these models directly through APIs, fine-tune them with custom data, or build applications using Vertex AI's generative AI tools. The platform also supports custom model training using popular frameworks like TensorFlow, PyTorch, scikit-learn, and XGBoost, with managed infrastructure that handles resource provisioning and scaling automatically.
Key capabilities and features of Vertex AI include:
- AutoML – Automated model training that enables users with limited ML expertise to build high-quality models for vision, text, and tabular data.
- Vertex AI Pipelines – Orchestration of ML workflows using Kubeflow or TFX for reproducible, automated training pipelines.
- Feature Store – A centralized repository for sharing and reusing ML features across projects and teams.
- Model Registry – Version control and governance for trained models throughout their lifecycle.
- Prediction endpoints – Managed serving infrastructure for deploying models as scalable online or batch prediction services.
- Vertex AI Agent Builder – Tools for building AI agents and RAG-based applications with grounding capabilities.
For organizations already invested in the Google Cloud ecosystem, Vertex AI provides a natural platform for incorporating AI and machine learning into their products. Its tight integration with BigQuery, Cloud Storage, and other GCP services simplifies data workflows, while its managed infrastructure reduces the operational burden of running ML systems at scale.