With so many artificial intelligence (AI) tools out there—spurred on by tools like ChatGPT becoming the fastest-spreading tech platform in history—it's hard to figure out which one you truly need. We can help make this decision easier by introducing you to Vertex AI, Google's very own AI cloud-based platform.

The recent boom in AI models and technological advances reshapes how many businesses operate, with research suggesting that more than 30% of all workers could see a significant portion of their occupational tasks disrupted by generative AI; the only issue is that there are too many solutions to choose from. But we can help.

To make it easier to build machine learning (ML) models, Google created Vertex AI, a platform that makes all of Google's cloud offerings available in a single entity.

But what is Vertex AI, in essence? Is it worthy of your attention or is it just a flop? Discover the answer in this guide, as we'll explore why Vertex AI is so important to businesses and many other things.

Stay tuned!

What is Vertex AI?

Vertex AI is Google Cloud’s unified machine learning platform that helps businesses build, train, and deploy AI models at scale. You can customize large language models (LLMs) and access Google’s suite of AI tools through a single interface.

Through features like custom model training, AutoML, and other advanced capabilities, you can greatly boost productivity and scalability.

Plus, Vertex AI brings many Google Cloud services together in one tool, helping you work with data, deploy models, and operate production workloads. In a nutshell, Vertex AI is here to streamline your ML workflow and simplify your end-to-end model lifecycle.

It’s a solution that works for both experienced ML teams and teams getting started; research has documented that less experienced workers experienced the biggest gains from using AI.

But why is this tool so important for businesses?

How Vertex AI differs from Gemini

Google’s AI ecosystem includes both Vertex AI and Gemini, which serve different purposes:

• Gemini: Google’s multimodal AI models that process text, images, code, and video

• Vertex AI: The enterprise platform where you access, customize, govern, and deploy Gemini models

Think of Gemini as the engine and Vertex AI as the complete workshop for enterprise AI development.

Why is Vertex AI Important?

The generative AI market keeps growing. For instance, the number of active gen AI projects on Vertex AI has grown more than 7x; but what exactly makes Vertex so important?

Well, let’s see what it does:

• Streamlined development: This AI solution helps automate and standardize key parts of the development process, so teams can deploy models faster with fewer handoffs.

• Higher speed: By reducing friction across training and deployment, teams can iterate faster—and move from prototypes to production more reliably.

• Consistency: When you centralize tooling and processes, you can improve repeatability across environments and reduce avoidable errors.

• Scalability: You can design all kinds of AI apps with Vertex, from small proofs of concept to enterprise-grade deployments.

As you can see, Vertex AI provides plenty of benefits—but what are the features that help it become so useful?

Find the answer in the next section.

What are the Google Cloud Vertex AI Key Features?

To understand what makes this Google Cloud platform so useful for building and deploying models, let’s look at its features:

• AutoML: This functionality is for developers who don’t have a lot of experience with ML but want to build custom ML models, an important feature considering a significant portion of the public has a low awareness of AI. It works for various Google apps, including Vertex AI, AutoML Tabular, and AutoML Image.

• Vertex AI pipelines: With this feature, you can automate, monitor, and orchestrate end-to-end ML workflows to reduce repetitive work.

• Pre-trained APIs: With Vertex AI, integrating capabilities like translation and natural language processing is easier—so teams can meet business requirements without rebuilding everything from scratch.

• Data and AI integration: Vertex AI plays nicely with plenty of tools, like BigQuery ML, Dataproc, or Dataflow. So, you can build or export data from BigQuery directly through Vertex AI Workbench. Moreover, it integrates with open-source frameworks like PyTorch and TensorFlow.

• Model versioning and monitoring: Vertex AI helps keep track of model versions and monitor performance through evaluation metrics so you can proactively identify issues.

So, Vertex AI has plenty of capabilities; the only issue is: what are you going to use them for?

That’s a question we’ll answer in the following section.

Vertex AI Enterprise Security and Governance

Enterprise AI requires robust security and governance controls, and Vertex AI delivers these through Google Cloud’s enterprise-grade infrastructure.

Key Security Capabilities

Data Privacy
Control where data is stored and processed to meet organizational and regulatory requirements.

Access Controls
Use granular Identity and Access Management (IAM) permissions to manage access to models, datasets, and training environments.

Auditability
Track model versions, training data, and performance metrics to support transparency, compliance, and reproducibility.

VPC Controls
Enforce VPC Service Controls service perimeters and private network configurations to prevent data exfiltration and maintain secure communication between resources.

With these capabilities, Vertex AI helps enterprises maintain security, compliance, and trust throughout every stage of the AI lifecycle—from data ingestion to model deployment.

These features help ensure your AI applications are both powerful and trustworthy by design.

What is Vertex AI Used For?

Usually, people use artificial intelligence tools in a wide range of areas, the most popular being in customer service (56%) and fraud management (51%). But it has plenty of other uses, such as product recommendations, content production, and others.

Going by this logic, is Vertex AI good at something? It sure is; here are its most common use cases:

• Improved forecasting: Use Vertex AI to improve the accuracy of real-time predictions, since it can analyze and interpret data quickly.

• Streamlined monitoring: Vertex AI monitors model performance, from development through production, so teams can catch issues earlier.

• Shared ML features: Through the Vertex AI Feature Store, you can manage and reuse the ML features you need (especially helpful when multiple teams ship models across products).

• Enhanced development process: With Vertex AI, it’s simpler to configure and automate ML workflows so you can speed up delivery.

As you can see, Vertex AI has multiple use cases that make it a practical addition to your business.

How to use Vertex AI and machine learning (ML) workflow

Ready to start with Vertex AI? Here’s the step-by-step ML workflow:

1. Data preparation

Extract and clean your dataset, then perform exploratory data analysis (EDA) to understand your data structure. Use Vertex AI Workbench to visualize and prepare your data for training.

Key tasks include:

• Data transformations and feature engineering

• Splitting data into training, validation, and test sets

2. Model training

Choose your training approach based on your technical expertise and business needs:

• AutoML: No-code model training for image, text, and tabular data

• Custom frameworks: Write your own code using familiar ML libraries

• Vertex AI Experiments: Test multiple approaches to find the best performer

• Vertex AI Vizier: Automated hyperparameter tuning for complex models

3. Model evaluation and iteration

Evaluate model performance using metrics like precision and recall. Compare different approaches to identify your best-performing model before deployment.

4. Model serving

Deploy your trained model using one of these methods:

• Pre-built containers: Quick deployment option

• BigQuery ML: Direct integration with data warehouse

• TensorFlow runtime: Optimized for TensorFlow models

• Vertex AI Feature Store: Reusable ML features

• Vertex Explainable AI: Model interpretability tools

5. Model monitoring

Monitor performance with Vertex AI Model Monitoring to catch data drift and performance issues. Set up alerts to know when retraining is needed.

Getting the most from your AI investments

Vertex AI provides powerful AI development capabilities, but your AI is only as good as the knowledge it uses. Outdated or incorrect information creates unreliable answers and user distrust, a significant issue when a recent study found that many people are more concerned than excited about the increasing use of AI.

This is where an AI Source of Truth becomes essential:

• Connect: Guru links all your company’s trusted information and permissions

• Verify: Creates a single, verified company brain

• Ground: Provides trustworthy knowledge for platforms like Vertex AI (and other AIs) with citations and access controls

The result? Policy-enforced, permission-aware answers with auditability across your AI investments.

See how Guru delivers trusted answers across your tools and builds trust into every answer.