Vertex AI is a machine learning (ML) platform provided by Google Cloud Platform (GCP). It offers a unified and managed environment for developing, deploying, and scaling ML models. Vertex AI simplifies the process of building ML models by providing a range of tools and services.

Here are some key features of Vertex AI:

1. Data preparation: Vertex AI provides capabilities for data ingestion, preprocessing, and transformation. It offers tools for exploring and visualizing datasets, as well as data labeling services.

2. Model development: Vertex AI supports various ML frameworks like TensorFlow, PyTorch, and scikit-learn. It provides a JupyterLab interface for building and testing ML models using these frameworks.

3. AutoML: Vertex AI includes AutoML capabilities, which enable users to automatically build ML models without extensive coding or ML expertise. It offers AutoML services for vision, language, structured data, and tabular data.

4. Model deployment: Vertex AI allows you to deploy your ML models as RESTful APIs with a few simple steps. It handles the underlying infrastructure, scaling, and monitoring, making it easier to deploy models at scale.

5. Training and serving: Vertex AI provides distributed training capabilities, allowing you to train ML models on large datasets using multiple machines. It also offers online and batch prediction services for serving predictions in real-time or asynchronously.

6. Monitoring and management: Vertex AI includes tools for monitoring and managing ML models. It offers logging, metrics, and alerts for tracking model performance and health. You can also monitor and manage deployed models using the Vertex AI Console.

7. Integration with other GCP services: Vertex AI integrates with other GCP services like BigQuery, Cloud Storage, and Cloud Pub/Sub, making it easy to ingest and process data from various sources.

Overall, Vertex AI aims to streamline the end-to-end ML workflow, from data preparation to model deployment and management, making it easier for developers and data scientists to leverage the power of ML in their applications.