Architecting enterprise AI on google's unified platform

Its main advantage is a unified platform for the entire ML lifecycle. This eliminates the need to stitch together separate services for data preparation, training, deployment, and monitoring, significantly accelerating the process from experimentation to production.

Yes. Vertex AI supports traditional custom model development (using frameworks like TensorFlow and PyTorch) and provides access to Google's state-of-the-art Generative AI models (like the Gemini family) via Vertex AI Studio and Model Garden for easy testing, tuning, and deployment.

Vertex AI offers purpose-built MLOps tools, including Vertex AI Pipelines for workflow automation, Model Registry for version management, and Model Monitoring to check for training-serving skew and prediction drift in production.

Yes, Vertex AI includes AutoML features. These tools allow users with limited ML expertise to train high-quality models for tabular, image, or text data with minimal code, as the platform handles model architecture selection and hyperparameter tuning automatically.

Model Garden is a catalog within Vertex AI where users can discover, test, customize, and deploy models and select open-source large language models (LLMs) and assets for use in their applications.

Vertex AI supports enterprise-grade security features like VPC Service Controls for data exfiltration prevention, Customer-Managed Encryption Keys (CMEK) for data at rest, and features for ensuring data residency and HIPAA compliance for sensitive workloads.