Artificial intelligence (AI) and machine learning (ML) are transforming the technology landscape, and Google has been there from the beginning, leading this revolution with advanced tools for businesses.
One of them is Vertex AI, the newest member of the Gemini ecosystem and a powerful platform that allows developers and data scientists to build and deploy AI and ML models more efficiently.
Let’s explore together what Vertex AI is, its main uses and who can benefit from its implementation. Plus, find out how Vertex AI differs from Google AI Studio and we’ll also tell you how much it costs.
What is Vertex AI?
Vertex AI is a platform within the Google Cloud ecosystem designed to develop and deploy AI and ML applications.
It integrates more than 100 foundational models covering areas such as search and conversation, making it easy to quickly develop generative applications and create custom ML models.
Vertex AI stands out for offering a unified environment where it is possible to carry out the entire life cycle of a model, from data preparation to deployment and monitoring.
Instead of needing multiple tools for each step of the model building process, Vertex AI offers a centralized platform with pre-configured tools, such as AutoML, that allows users without programming experience to train models from images, text, or structured data.
Additionally, its Pipelines make it easy to automate ML workflows, eliminating the need to manage physical infrastructure.
What is Vertex AI used for?
Vertex AI has diverse applications in sectors such as healthcare, financial services, manufacturing, and retail. Some of its most common features include:
- Development of machine learning models: Users can create models from historical data to perform predictions, trend analysis or data classification in real time.
- Generative AI applications: Ideal for creating applications that generate new content, such as text or images, using pre-trained AI models.
- Data analysis and management: Vertex AI makes it easy to ingest and organize large volumes of data, including tools like Feature Store, which allows you to store and share ML features between different projects.
- Business process optimization: Companies like Wayfair use Vertex AI to improve the efficiency of their supply chain by automating processes and predicting demand.
Who can benefit from Vertex AI?
Vertex AI is designed to be accessible to both experienced data scientists and developers with limited machine learning knowledge.
Some of the profiles that can make the most of this platform include:
Analysts and data scientists
Vertex AI’s ability to train and deploy custom models, as well as its integration with popular frameworks such as TensorFlow and PyTorch, makes it an essential tool for artificial intelligence research and development.
Software developers
For those looking to incorporate AI into their applications, Vertex AI provides tools such as pre-trained APIs for computer vision, natural language processing, and video analytics.
Companies
Any organization looking to optimize their processes, predict behaviors, or automate tasks through AI models can benefit from using Vertex AI, as it simplifies the creation and management of large-scale models.
What is the difference between Vertex AI and Google AI Studio?
Although Vertex AI and Google AI Studio share the same goal of making AI easier to use, they are platforms designed for slightly different audiences and uses. Below are the main differences:
Focus and complexity
Vertex AI is designed for users looking to develop advanced, custom AI solutions. Provides more control over the lifecycle of models, from data ingestion to deployment.
Google AI Studio, on the other hand, is geared more toward less technical users, providing a simpler interface to explore and experiment with pre-trained AI models.
MLOps Tools
Vertex AI includes a complete set of tools for MLOps (Machine Learning Operations), allowing you to manage the lifecycle of models, their updating and continuous monitoring. These tools are not as developed in Google AI Studio.
Automation and customization
One of the main advantages of Vertex AI is its ability to automate the entire workflow using Pipelines, something that is not as integrated in Google AI Studio.
Vertex AI also allows for a high level of customization, both in building models from scratch and in integrating with other tools and frameworks. Google AI Studio, being simpler, limits these options.
How much does Vertex AI cost?
The Vertex AI pricing model varies depending on the use of the platform. Costs can fluctuate depending on the amount of data that is processed, stored or used in different models.
Below are some of the aspects that influence the price:
AutoML and personalized training
The cost of training models with AutoML depends on the type of model and the amount of data.
Custom training, which allows the use of external frameworks such as TensorFlow, also has an associated cost depending on the processing time and hardware resources used.
Processing and generation
The amount of data stored in Google Cloud and the processing performed by Vertex AI are factors that directly affect the price. For generative AI applications, the cost is adjusted based on the volume of data and requests made to the models.
Google offers a table to estimate the costs of using Vertex AI, depending on the use and the chosen node, allowing companies to adapt the use of the platform to their budget.
The all-in-one solution for AI professional development
Vertex AI is positioned as a comprehensive solution for those looking to develop AI and ML models within a scalable and highly customizable environment.
With its ability to automate processes, integrate popular tools, and manage the entire lifecycle of a model, it is a powerful platform for both data scientists and software developers.
Despite sharing some goals with Google AI Studio, Vertex AI sets itself apart with its focus on customization and support for larger projects.
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