App๐Ÿš€
Get Our Mobile App

How to Use Google Cloud Vertex AI

How to Use Google Cloud Vertex AI


Google Cloud Vertex AI is a managed machine learning platform that helps businesses build, deploy, and scale AI models faster and with less effort. It offers a unified interface and powerful tools for both beginners and experienced ML engineers to take projects from experimentation to production.

With Vertex AI, organizations can leverage Google’s powerful infrastructure and pre-trained models, or bring their own custom models for scalable training, tuning, and inference.

What Is Vertex AI?

Vertex AI is part of the Google Cloud ecosystem and combines Google’s machine learning services under one platform. It includes:

  • Model training and deployment tools

  • Pre-trained APIs for vision, text, and language

  • AutoML capabilities for low-code development

  • MLOps integration for pipeline automation

  • Custom model support with Jupyter notebooks and Docker containers

Vertex AI supports every stage of the ML lifecycle — from data preparation to continuous deployment and monitoring.

To explore more AI development tools and cloud-based services, visit I Need AI, a smart directory for AI tools and platforms.

Getting Started with Vertex AI

To start using Vertex AI:

  1. Visit the official page: cloud.google.com/vertex-ai

  2. Sign in with your Google Cloud account

  3. Create a new project in the Google Cloud Console

  4. Enable the Vertex AI API

  5. Set up a billing account if required

You can now access the Vertex AI dashboard, where all development, deployment, and monitoring tools are available in one place.

Training Models in Vertex AI

Vertex AI supports both AutoML and custom model training.

  • AutoML is for users who want to upload datasets and let Google train models automatically. You simply select your use case (e.g., image classification or text sentiment), upload data, and let the system do the rest.

  • Custom Training allows advanced users to bring their own training code in frameworks like TensorFlow, PyTorch, or Scikit-learn.

You can use Jupyter notebooks or create training pipelines using the built-in workflow editor.

Once training is complete, models are saved and versioned automatically for deployment.

Deploying and Serving Models

After training, Vertex AI allows you to deploy models to managed endpoints for real-time predictions or batch inference.

  • Set up an endpoint with autoscaling enabled

  • Deploy your trained model with minimal configuration

  • Start making predictions via REST API or SDK

This managed deployment helps reduce infrastructure setup time and ensures high availability.

Pre-trained APIs

Vertex AI provides ready-to-use APIs for common tasks like:

  • Vision AI: Image labeling, object detection, OCR

  • Natural Language AI: Entity extraction, sentiment analysis

  • Translation AI: Multilingual content translation

  • Speech AI: Speech-to-text transcription

These APIs are ideal for businesses that need fast results without training models from scratch.

MLOps and Pipelines

For continuous training and delivery, Vertex AI integrates MLOps features such as:

  • Pipelines: Build CI/CD-style ML workflows

  • Feature Store: Manage and reuse features across projects

  • Model Monitoring: Track model performance and detect drift

  • Metadata tracking: Organize and reproduce experiments

This makes Vertex AI production-ready and scalable for enterprise teams.

To discover more platforms offering similar lifecycle management features, browse I Need AI, which features cloud AI tools for developers, researchers, and businesses.

Who Should Use Vertex AI?

Vertex AI is built for:

  • Developers and ML engineers building scalable AI applications

  • Data scientists needing a managed environment to experiment

  • Businesses looking to automate processes with AutoML and APIs

  • Enterprises needing end-to-end MLOps and governance

Whether you're starting with machine learning or scaling to serve millions of predictions, Vertex AI adapts to your workflow.

Final Thoughts

Google Cloud Vertex AI simplifies machine learning development by combining automation, flexibility, and scalability. With support for both low-code and expert-level development, it opens the door for teams of any size to create powerful AI solutions. If you're looking to accelerate your AI adoption in the cloud, Vertex AI is a smart and efficient platform to build on.