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vertex ai

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Key features of Vertex AI include:

  1. Unified Platform: Vertex AI aims to provide a unified platform for ML tasks, bringing together data preparation, model training, deployment, and monitoring into a single environment.
  2. AutoML (Automated Machine Learning): Vertex AI offers AutoML capabilities, allowing users to build and deploy machine learning models without extensive knowledge of machine learning or programming.
  3. Custom Model Training: For users with specific requirements or expertise in machine learning, Vertex AI supports custom model training using popular ML frameworks like TensorFlow and PyTorch.
  4. Model Deployment: The platform facilitates the deployment of models at scale, making it easier to integrate them into applications and services.
  5. Monitoring and Management: Vertex AI includes tools for monitoring model performance and managing models throughout their lifecycle. This includes features for versioning models and rolling out updates.
  6. Explainability and Fairness: Vertex AI provides tools for model explainability, helping users understand how models make predictions. It also includes features to assess and mitigate bias in models.
  7. Data Labeling: The platform supports data labeling services, which is crucial for training supervised machine learning models.
  8. Integration with Google Cloud Services: Vertex AI is integrated with other Google Cloud services, allowing users to leverage features such as Cloud Storage, BigQuery, and more.

To get the latest and most accurate information about Vertex AI, I recommend checking the official Google Cloud documentation or contacting Google Cloud support.