Vertex AI
By Google Cloud
Vertex AI is Google Cloud's unified machine learning platform for building, training, deploying, and managing both custom ML models and generative AI applications, including access to the Gemini model family.
Definition
Vertex AI is Google Cloud's unified machine learning platform for building, training, deploying, and managing both custom ML models and generative AI applications, including access to the Gemini model family.
Overview
Vertex AI is Google Cloud's end-to-end platform for the machine learning lifecycle. It brings together tools that were historically scattered across separate products — data labeling, notebook environments, custom model training, hyperparameter tuning, and model deployment — under a single managed service, alongside a Model Garden that offers ready-to-use foundation models including Gemini and select open-source options. For generative AI specifically, Vertex AI provides managed access to Gemini for text, chat, and multimodal generation, along with tooling for fine-tuning, grounding responses in enterprise data via retrieval, and evaluating model outputs against custom metrics. Its Agent Builder tooling also supports constructing AI agent and RAG-style applications that combine an LLM with a company's own documents and APIs, a pattern covered in depth in Retrieval-Augmented Generation. Beyond generative AI, Vertex AI remains a full MLOps platform: teams can train custom models with AutoML or custom containers, track experiments, version datasets and models, and deploy endpoints with autoscaling, monitoring, and explainability built in — much of which overlaps with the discipline taught in MLOps & Model Deployment. Because it sits inside Google Cloud's broader ecosystem, Vertex AI integrates natively with services like BigQuery for data and IAM for access control, making it the typical landing spot for teams that start prototyping in the lighter-weight Google AI Studio and later need production-grade scale, security, and governance.
Key Features
- Model Garden with access to Gemini and curated open-source foundation models
- Managed fine-tuning, prompt tuning, and RLHF-style alignment workflows
- AutoML and custom training for traditional and deep learning models
- Agent Builder for constructing retrieval-augmented and tool-using AI agents
- Feature Store, pipelines, and experiment tracking for full MLOps coverage
- Model monitoring, explainability, and drift detection for deployed endpoints
- Tight integration with BigQuery, Cloud Storage, and Google Cloud IAM