What Is Vertex AI? Features, Uses, and Benefits

Google’s Vertex AI is Google Cloud’s unified platform for building, deploying, and scaling machine learning (ML) and artificial intelligence (AI) models. It supports custom model training, AutoML, and foundation models—offering tools for data preparation, model monitoring, MLOps, and real-time inference within one end-to-end system.

As AI adoption accelerates across industries, more teams are looking for tools that simplify complex machine learning workflows and bring models to production faster. That’s where Vertex AI comes in. Whether you're building AI-powered chatbots, predictive analytics models, or generative AI applications, Vertex AI gives you the infrastructure and flexibility to get there—without managing compute resources or stitching together multiple platforms.

Built for both beginners and experienced data scientists, Vertex AI supports a wide range of frameworks, including TensorFlow, PyTorch, and open-source models. Its fully managed services reduce setup time and help teams focus on what matters most: delivering high-quality AI solutions that scale.

What is Vertex AI?

Vertex AI is a unified machine learning platform used for building and using generative AI systems. Vertex AI includes over 100 foundation models, along with Search and Conversation and other AI solutions. Vertex AI is useful for accelerating ML development, building generative apps quickly, and training and fine-tuning ML models—all on the same platform.

In the Google Cloud ecosystem, Vertex AI’s role is to offer a scalable and efficient solution for the development and deployment of ML models. Google Cloud offers a number of AI and machine learning services to help users build generative AI applications, improve customer service through the use of AI agents, and generate AI-powered code across different Google Cloud tools. In total, Google Cloud services include over 150 products.

The Vertex AI platform is used by data scientists and machine learning engineers who are looking to automate or streamline their machine learning projects. Much of the automation happens through Vertex AI Pipelines, which orchestrate your ML lifecycle or workflow in a serverless fashion. Other key functionalities include data preparation, model training, model evaluation, and more.

Pricing is based on compute usage, model type, and API calls, which vary across products like AutoML, foundation models, and custom model training. In each scenario, pricing correlates with the amount of data being used, stored, or processed.

Google Cloud Vertex AI has a streamlined machine learning workflow, making the ML journey more approachable for data scientists. It also integrates with popular machine learning frameworks such as TensorFlow and PyTorch. Its practicality and accuracy make it a popular choice for companies looking for cutting-edge technology.

Key features of Vertex AI

Now that we have an overview of what Vertex AI is and what it does, we’ll discuss several of the key features of Vertex AI in greater detail.

Machine learning models

Vertex AI hosts a wide spectrum of machine learning models, including pretrained models and custom model creation. Each model has a different name, modality, and description. Broadly speaking, foundation models are trained large models that are customizable for different tasks, while fine-tunable models can be adjusted with a pipeline or custom notebook. Prebuilt, task-specific solutions are also available and are both ready to use and customizable based on the data you have on hand.

Model Garden provides access to a wide selection of pretrained models from Google and the open-source community. This includes Gemini, Google’s most advanced large language model (LLM), which is natively integrated into Vertex AI for text generation, summarization, and chat-based applications.

As you can imagine, these models offer a wide range of applications in different industries and sectors. Typically, ML models are employed for art creation, text generation, and image synthesis. They can also retrieve real-time data, sort it into preselected categories, and make predictions or identify trends based on the data presented.

Fully managed tools

Vertex AI provides several helpful tools to take the grunt work out of the machine learning process. For example, AutoML helps you train data from images, texts, and videos while eliminating the need for code or prepared data splits. Custom training allows you to use your preferred ML framework and select from different hyperparameter tuning options. Finally, generative AI offers access to Google’s selection of generative AI models within multiple modalities. You’ll be able to deploy them inside all your AI-powered applications.

Part of what makes Vertex AI easy to use is that its ML models require no physical infrastructure administration; you don’t have to provide or manage your own servers. Vertex AI will also take care of all necessary logging and queuing. It also offers several security features to protect your data and limit unauthorized network access.

Data science workflow

The data science workflow supported by Vertex AI includes essential stages like data preparation, model training, and evaluation. Usually, this is done using structured (or tabular) data. Each component of the process is efficient and user-friendly for maximum optimization.

Vertex AI Workbench is a Jupyter-based development environment integrated with Google Cloud, allowing teams to build, train, and deploy models without switching between tools. It supports Python, TensorFlow, PyTorch, and tight integration with BigQuery and Cloud Storage.

Data structuring and preparation require setting up your input source and adding different weights to the training data. Your metrics must conform to a list of requirements, including standards for size, number of columns and rows, and the data format.  

Google Cloud Vertex AI provides two different methods for model training. With AutoML, beginners can train models even with little prior knowledge or experience. If you would rather customize or create your own training models using any ML framework, custom training allows you to do so.

Regardless of what method you use, you’ll be able to evaluate the data using Vertex AI afterward. This starts with uploading your test dataset to a platform like BigQuery or Cloud Storage and setting up specific AI IAM permissions in your default Compute Engine account.

Customization and integration

Vertex AI includes multiple customization options, such as the ability to integrate with other tools. Vertex AI works with popular platforms such as Slack, Google Sheets, Typeform, Calendly, YouTube, and more. Vertex AI also includes functionality with other ML frameworks like TensorFlow, GPTConsole, Slingshot, Cameralyze, and Kedro.

This all comes in addition to the ability to customize Vertex AI to meet your unique needs. Vertex AI offers different functions for users with limited AI knowledge and experience. Still, the ability to tailor solutions and systems to specific applications is a nice bonus for anyone with the ability to build and develop their own models.

MLOps tools

Machine learning operations (MLOps) is a core function of ML engineering. MLOps has to do with the process of streaming and producing machine learning models in addition to upholding and monitoring them on an ongoing basis. Usually, this process requires attention from data scientists and machine learning engineers.

Vertex AI’s MLOps tools are instrumental in the automation and streamlining of model deployment and monitoring. These tools will help you implement various MLOps tools across your ML workflows while also improving operations over time through predictive monitoring, alerting, and diagnosis.‍

As AI development matures, companies are shifting from experimental models to production-ready systems. That’s why platforms like Vertex AI are increasingly focused on MLOps—bringing together version control, monitoring, governance, and retraining into one continuous pipeline. This shift makes machine learning workflows more scalable and sustainable across real-world applications.

Getting started with Vertex AI: No ML experience needed

If you’re new to machine learning or just starting to explore AI development, Vertex AI offers several beginner-friendly tools to help you get up and running—no coding background required.

Start with AutoML, which lets you train high-quality models using your own datasets through a simple interface. For inspiration and ready-to-use resources, browse the Model Garden, where you’ll find pretrained models for common tasks like image classification, text summarization, and natural language processing.

If you’re ready to try hands-on training, Vertex AI Workbench offers a Jupyter-based environment with guided notebooks and tutorials. It’s an easy way to learn the basics, experiment with sample data, and build your first AI-powered application in the Google Cloud ecosystem.

What can you use Vertex AI for?

Clearly, Vertex AI has a number of capabilities, but what applications do these functionalities have? We’ll discuss different scenarios below.

Data management and analysis

Vertex AI eases the data ingestion process from sources like Cloud Storage and BigQuery. Specifically, the Vertex AI data labeling feature enhances prediction accuracy and helps in generating high-quality training data. You can import both labeled and unlabeled data in addition to adding new labels and deleting existing labels from previously imported datasets.

In addition, the Vertex AI Feature Store is a fully managed, feature-rich repository that facilitates the serving, reusing, and sharing of ML features. It acts as a central location for organizing and storing different ML features. This can streamline data management and data analysis in machine learning projects ​while also making it easier for organizations to find and share a wide range of ML applications.

Pretrained APIs

APIs enable different forms of software to communicate in real time with each other. Pretrained APIs provided by Vertex AI cover domains such as vision, natural language processing, and video analysis. You can enable these pretrained APIs in your Google Distributed Cloud Hosted (GDCH) console after signing in and confirming any required prerequisites are verified.

These APIs can be seamlessly integrated into existing applications, and they make building new applications easy for use cases like translation or speech-to-text. Use cases may include conversational assistants, automated workflows, and search engines, along with data ingestion and feature extraction supporting gradual model improvement. Once deployed, these APIs are served via endpoints, allowing real-time inference and seamless integration into existing systems and applications.

Industry-specific applications

Vertex AI is highly versatile, with applications in a wide range of industries, including health care, financial services, manufacturing, and retail. Doctors can quickly extract data from patient records, financial analysts can measure performance and make predictions, and consumer goods organizations can accurately forecast future demand. While Vertex AI has value in many industries, these are a few of the most notable uses.

Organizations can employ Vertex AI for predictive analytics, fraud detection, and other machine learning-driven solutions tailored for specific industries. The ability to customize solutions to meet individual needs further expands the possibilities.

Optimizing supply chain operations

Companies like Wayfair have harnessed Vertex AI for optimizing supply chain operations. They find features like Pipelines, Hyperparameter Tuning, and Experiments are especially helpful for enhancing the efficiency of supply chain processes. They continue to look for ways to speed up the development of their models while also lowering maintenance efforts and enhancing the overall reliability of the system.

Automated machine learning (AutoML)

The concept of AutoML is significant for reducing the manual effort required in model development. Vertex AI’s AutoML features empower users to develop high-quality models with less manual intervention. These models are a tremendous option for first-time users and developers of AI with limited prior knowledge.

Benefits of Vertex AI

The benefits of using Vertex AI are many; let’s go over a few of the most notable:

• Unified platform. Vertex AI integrates several functions—including data preparation, model training, monitoring, and deployment—into the same platform, reducing complexity and making management and oversight easier.
• Support for open-source models. By incorporating open-source models, Vertex AI equips users to enhance their productivity and more effectively scale their workloads.
• Simplicity and scalability. Vertex AI’s models are not overly complicated or intricate, making it easy for users to build models using their own training data or customize a solution that meets their needs.
• Efficient infrastructure. Vertex AI’s scalable, cost-effective infrastructure allows for quick orchestration and easy management of large data clusters.
• Seamless data-to-AI integration. Since Vertex AI’s tools are fully managed, they are easy to integrate and deploy within a variety of applications. You can browse open-source frameworks and models in Model Garden, or you can install extensions that allow your models to retrieve data from different applications in real time.

Limitations and challenges of Vertex AI

Like all AI applications, Vertex AI isn’t perfect. Drawbacks can include challenges such as ensuring data privacy and security and preventing model bias. That said, Google has taken steps to address some of these challenges. You can also avoid or mitigate these issues by following best practices and using AI tools available within Vertex AI. These include implementing IAM policies to regulate access across teams and using auto-scaling to reduce costs.

Who should use Vertex AI?

Vertex AI is ideal for teams already using Google Cloud Platform (GCP) or working with BigQuery, Looker, or Cloud Storage. Compared to AWS and Azure, it stands out for its integration with open-source frameworks, accessible AutoML, and strong support for generative AI. It’s a smart choice for fast-moving teams who want to iterate quickly and scale efficiently.

Find AI engineers on Upwork

Vertex AI is a unique ML solution with several potential uses and applications. With a wide range of features, many users will find benefit and value from incorporating it into their AI workflow. As you get started with Vertex AI, consider setting aside time to learn about the different features and consider what applications might best benefit your workflow.

If you’re looking to engage an AI engineer to work with your team, you’ll find many experienced and talented professionals on the Upwork marketplace. There, you can also find AI talent to fill nearly any skills gaps your team might have.

Check out Upwork for your next job in AI or if you’re looking to add someone to your team who can help you incorporate and leverage AI throughout your entire project life cycle.

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.