Navigating the Frontier of Enterprise AI: Key Services on Vertex AI – Part 1

Vertex AI is Google Cloud’s unified, fully-managed AI platform that brings everything you need to build and scale enterprise AI in one place. From generative AI to traditional ML, Vertex AI accelerates development with a single environment, powered by models like Gemini, and tools for training, tuning, and deploying at scale.

This blog is a series of two parts. In the first part, we will talk about core services, Vertex AI Agent Builder, and Notebooks.

🎥 Prefer watching instead of reading? You can watch the NotebookLM podcast video with slides and visuals based on this blog here.

1. Core Services

1.1 Model Garden

The Model Garden is your one-stop shop for discovering, customizing, and deploying AI/ML models.

• 200+ models: Access a curated library of Google’s latest (Gemini, Imagen, Veo, Chirp), partner models (Anthropic’s Claude), and open-source options (Gemma, Llama 3.2, Mistral). 👉 See our previous blog about Anthropic’s Claude
• Easy deployment: Tune and deploy with just a few clicks, backed by Vertex AI’s MLOps integrations.
• Enterprise-grade security: All models undergo security testing and authenticity checks.

💡 Think of it as the App Store for enterprise AI models.

🌟 To learn more about Model Garden, watch the video below:

1.2 Vertex AI Studio

Vertex AI Studio makes it simple to experiment, prototype, and deploy generative AI apps.

• Rapid prototyping: Test models in a chat-like interface.
• Gemini-ready: Use multimodal Gemini models with text, images, video, music, or code.
• Customization made easy: Tune models with your own data, adjust prompts, and control creativity with parameters like temperature.
• Extensions + MLOps: Connect models to live data or APIs, then manage and scale with Google’s managed infrastructure.

💡 Perfect for teams who want to move from idea to working prototype in hours, not weeks.

🌟 To learn more about Vertex AI Studio, watch the video below:

1.3 GenAI Evaluation

Building AI is one thing, trusting it is another. Vertex AI includes GenAI Evaluation to help you measure performance.

• Adaptive Rubrics: Automatically generate pass/fail tests tailored to each prompt.
• Multiple methods: Use metrics like BLEU/ROUGE, static rubrics, or your own Python functions.
• Real-world testing: Evaluate with production data, synthetic prompts, or custom datasets.
• Flexible access: Run evaluations in-console with reports or programmatically via Python SDK.

💡 This ensures you pick the right model, refine prompts, and validate results before scaling.

🌟 To learn more about GenAI Evaluation, watch the video below:

1.4 Tuning

Pretrained models are powerful, but tuning unlocks enterprise value.

• Why tune? Improve accuracy, consistency, and cost-efficiency for domain-specific tasks.
• Methods:
  
  • Adapter tuning: lightweight, fast, and cost-effective.
  • Full fine-tuning: deeper, resource-intensive, but best for complex use cases.
• Supported models: Gemini 2.5 Flash, Gemini 2.5 Pro, plus support for text, images, audio, video, and more as input.
• Getting started: Just 100–500 examples (in JSONL) can meaningfully customize a model.

💡 Whether it is legal documents, healthcare claims, or retail chatbots, tuning ensures the model speaks your business language.

🌟 To learn more about Tuning, watch the video below:

2. Vertex AI Agent Builder

Moving beyond individual models, Vertex AI Agent Builder enables enterprises to build intelligent, multi-agent ecosystems. It provides the tools to transform complex business processes into scalable, secure, and collaborative agent-driven solutions. With enterprise-grade governance and integration, Agent Builder ensures agents can work together while staying aligned with security and compliance needs.

2.1 Agent Garden

The Agent Garden is the starting point for exploring pre-built agents and tools.

• Pre-built agents: End-to-end solutions for use cases like customer service, data analysis, or IT automation.
• Tools library: Modular components (e.g., API connectors, search utilities) you can integrate into your custom agents.
• Accelerated development: Sample agents are ready to deploy via the Agent Development Kit (ADK), so you do not need to start from scratch. 👉 See our previous blog about ADK

💡 Think of it as the “model garden” but for agents and their building blocks.

2.2 Vertex AI Agent Engine

The Agent Engine is the fully managed runtime for deploying, scaling, and managing AI agents in production. It removes the overhead of infrastructure so teams can focus on agent logic.

• Framework-agnostic: Supports ADK, LangChain, LangGraph, and even custom agents built with CrewAI, AG2, or LlamaIndex.
• Core runtime services:
  
  • Sessions: Maintain conversation context across interactions.
  • Memory Bank: Long-term storage of preferences and context for personalized responses.
  • Code Execution: Run code safely in an isolated sandbox.
• Interoperability via A2A Protocol: Agents can collaborate across frameworks using the Agent2Agent (A2A) standard, donated by Google to the Linux Foundation. Agents coordinate tasks securely without sharing sensitive internal logic. 👉 See our previous blog about A2A

💡 This makes it possible to deploy agents that do not just respond, they remember, adapt, and collaborate.

2.3 RAG Engine

The Retrieval-Augmented Generation (RAG) Engine is Google’s managed framework for context-aware AI.

• Why it matters: RAG reduces hallucinations by grounding model responses in your organization’s private data.
• Automated orchestration: Handles ingestion, chunking, embeddings, indexing, retrieval, and generation in sequence.
• Flexibility: Works with multiple vector databases, including Vertex AI Vector Search, Feature Store, and Cloud Spanner.

💡 The RAG Engine turns LLMs into enterprise knowledge workers by connecting them to trusted, real-time information.

2.4 Vertex AI Search

Vertex AI Search is Google’s out-of-the-box solution for enterprise-grade search and RAG.

• Simplified workflows: No need to build custom pipelines; ETL, OCR, embeddings, and retrieval are all automated.
• Google-quality search: Built on decades of semantic search expertise, optimized for industries like commerce, media, and healthcare.
• Grounded responses: When paired with LLMs, Search ensures generative AI is tied directly to your enterprise data, improving accuracy and reliability.

💡 It is the fastest way to deliver Google-like search experiences inside your organization.

2.5 Vector Search

Underpinning Vertex AI Search and RAG is Vector Search, a high-performance engine built with Google Research’s ScaNN algorithm.

• Semantic search: Finds results based on meaning, not just keywords.
• Use cases: Product recommendations, conversational agents, and real-time personalization.
• Hybrid search: Combines dense (semantic) and sparse (keyword) embeddings for the best of both worlds.
• Proven at scale: The same technology powers Google Search, YouTube, and Google Play, now available for custom enterprise use cases.

👉 See our previous blog about Vertex AI Search and Vector Search

💡 Whether you are building next-gen recommendations or intelligent assistants, Vector Search delivers the speed and scale your business demands.

3. Notebooks

Behind every great AI model is experimentation, iteration, and hands-on development. To support this, Vertex AI provides powerful interactive notebook environments that integrate directly with Google Cloud services. These environments make it easier for data scientists and developers to move seamlessly from exploration to production.

Vertex AI offers two primary options: Colab Enterprise and Vertex AI Workbench. Both provide flexibility, scalability, and direct access to Google Cloud’s AI stack, whether you are training AutoML models or building custom deep learning workflows with TensorFlow or PyTorch.

3.1 Colab Enterprise

Colab Enterprise extends the simplicity of Google Colab into an enterprise-ready, managed environment inside Vertex AI. It is designed for rapid prototyping and experimentation while keeping everything secure and connected to Google Cloud.

• Notebook management: Create, rename, import, and delete notebooks directly in the Google Cloud console.
• Managed runtimes: Each notebook runs on a runtime with configurable machine types and disk sizes.
  
  • Default runtime: Comes preconfigured for easy access to Google Cloud services, often with end-user credentials enabled for quick interaction.
  • Runtime operations: Developers can start, stop, or delete runtimes as needed.
• Enterprise integration: With built-in Google Cloud authentication, Colab Enterprise makes it easy to connect notebooks to services like BigQuery, Cloud Storage, and Vertex AI training infrastructure.

👉 See our previous blog about Colab Enterprise

💡 Colab Enterprise is best for teams who need fast, lightweight experimentation without worrying about setup or infrastructure management.

3.2 Vertex AI Workbench

For more complex workflows, Vertex AI Workbench offers a full-featured, Jupyter-based environment built for end-to-end data science and ML development.

Core Environment and Functionality

• Prepackaged setup: Comes with JupyterLab and popular ML frameworks like TensorFlow and PyTorch preinstalled.
• Customizable instances: Choose CPU-only or GPU-enabled instances, configure machine types, add GPUs, and upgrade environments on demand.

Data Integration

• Cloud Storage: Browse and manage files directly in JupyterLab.
• BigQuery: Explore datasets, query tables, preview results, and load data directly into notebooks. Visualization support makes analysis seamless.

Workflow Automation with Executor

• Run notebooks as scheduled jobs or one-time tasks, even if the instance is shut down.
• Executions run on Vertex AI custom training infrastructure, with outputs saved automatically to Cloud Storage for easy sharing.

Customization and Security

• Version control: Sync notebooks with GitHub repositories.
• Custom environments: Add conda environments or start from custom containers for specialized frameworks or languages like R.
• Enterprise security:
  
  • VPC network support.
  • Customer-Managed Encryption Keys (CMEK).
• Cost optimization: Idle instances shut down automatically to avoid unnecessary charges.

💡 Vertex AI Workbench is ideal for enterprises that need scalable, secure, and customizable environments to power advanced AI development.

🌟 To learn more about Notebooks, watch the video below:

⭐⭐⭐

Vertex AI is more than just a collection of tools, it is a unified platform that empowers enterprises to innovate with AI at scale. From core services like Model Garden and Vertex AI Studio, where teams can access, fine-tune, and evaluate world-class models, to Vertex AI Agent Builder, which enables the creation of intelligent, multi-agent ecosystems with RAG capabilities and advanced search, the platform covers the full spectrum of modern AI development needs.

Meanwhile, interactive notebook environments such as Colab Enterprise and Vertex AI Workbench provide data scientists and developers with flexible, collaborative spaces to experiment, train, and iterate on models efficiently, while maintaining direct integration with Vertex AI’s ecosystem. Together, these capabilities make it easier than ever to move from concept to production, ensuring that AI initiatives are fast, scalable, and enterprise-ready.

Whether you are building generative AI applications, custom ML models, or complex AI agents, Vertex AI provides the infrastructure, tools, and governance required to deliver results confidently and securely.

Ready to accelerate your AI journey? Contact us today and start exploring Vertex AI and unlock the full potential of enterprise-grade generative AI.