Artificial intelligence (AI) is transforming the world in unprecedented ways. From healthcare to entertainment, from education to business, AI enables new possibilities and previously unimaginable solutions. However, developing and deploying AI applications can be challenging and complex, requiring specialized skills and resources that are not easily accessible to everyone.
That’s why Google has created Vertex AI. This generative AI platform simplifies and accelerates machine learning (ML) workflow. Vertex AI is a unified data and AI platform that lets you build, train, and scale ML models faster and easier, with fully managed tools for any use case. Whether you are a beginner or an expert, it can help you create high-quality AI applications with minimal effort and cost.
In this article, we will explore what it is, how it works, and what benefits it offers developers and businesses. We will also look at some of the key features and capabilities of it, such as generative AI, AutoML, custom training, MLOps, and data integration. Finally, we will show you how to get started with it and where to find more resources and information.
What is Vertex AI?
Vertex AI is Google’s generative AI platform that combines data engineering, data science, and ML engineering workflows into a single environment. It provides comprehensive tools and services for building, deploying, and managing ML models and AI applications.
It is designed to be flexible and scalable, allowing you to choose the level of automation and control that suits your needs. You can use it to:
- Build generative AI applications quickly and responsibly with Model Garden and Generative AI Studio.
- Train models using AutoML or custom training options with optimized infrastructure.
- Deploy models to production with end-to-end MLOps tools for automation and governance.
- Integrate data and AI with BigQuery, Dataproc, Spark, and other Google Cloud services.
Vertex AI is compatible with various ML frameworks and languages, such as TensorFlow, PyTorch, sci-kit-learn, XGBoost, Python, R, SQL, and more. You can use the Vertex AI SDK for Python to run the entire ML workflow in It Workbench, a Jupyter notebook-based development environment. Alternatively, you can use the Google Cloud Console, the gcloud command line tool, client libraries, or Terraform (limited support) to interact with Vertex AI.
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How does Vertex AI work?
Vertex AI provides a unified platform covering the entire ML lifecycle, from data preparation to model deployment and management. It has several components that help you create and run your ML projects. These components include:
- Vertex Data Labeling: A service that helps you generate high-quality labels for your data collection using human labelers or automated tools.
- Vertex Datasets: A service that helps you store, manage, and access your datasets for ML training and prediction.
- Vertex Feature Store: A service that helps you serve, share, and reuse features for ML models across different projects and teams.
- Vertex AutoML: A service that helps you train high-quality ML models without writing code or preparing data splits. Vertex AutoML supports tabular, image, text, and video data types.
- Vertex Custom Training: A service that helps you train custom ML models using your preferred framework and code. You can also optimize your models using hyperparameter tuning and distributed training options.
- Vertex Pipelines: A service that helps you automate and orchestrate your ML workflows using pipelines. You can use predefined templates or create custom pipelines using TensorFlow Extended (TFX) or Kubeflow Pipelines (KFP).
- Vertex Experiments: A service that helps you compare different ML techniques and track the performance of your models using experiments. You can use metrics, visualizations, and reports to analyze your results.
- Vertex Model Registry: A service that helps you store, version, and manage your trained models for deployment and hand-off to production.
- Vertex Prediction: A service that helps you deploy your models to online or batch endpoints for serving predictions. You can also monitor your models using logging and explainability features.
- Vertex Monitoring: A service that helps you track the health and performance of your models in production using monitoring dashboards. You can also set up alerts and notifications for any issues or anomalies.
- Vertex Workbench: A service that provides a Jupyter notebook-based development environment for running the entire ML workflow in it. You can use the Vertex AI SDK for Python to access your notebooks’ it components.
- Vertex Model Garden: A service that provides a collection of pre-trained and open-source models that you can use directly or customize for your use cases. You can also access generative AI models and APIs from Model Garden.
- Vertex Generative AI Studio: A service that helps you fine-tune and deploy generative AI models for your use cases using a simple UI. You can use models from Model Garden or upload your models to Generative AI Studio.
What are the benefits of Vertex AI?
Vertex AI offers several benefits to developers and businesses who want to leverage the power of AI for their projects. Some of the main benefits are:
- Simplicity: It simplifies the ML workflow by providing a single platform that integrates all the tools and services you need to build, train, and deploy ML models. You don’t have to worry about managing multiple products or services or switching between different interfaces or environments. Vertex AI also provides low-code and no-code options for building ML models, such as AutoML and Generative AI Studio, making it easy for anyone to get started with AI.
- Speed: Vertex AI accelerates the ML workflow by providing fully managed and optimized infrastructure that scales automatically to meet your performance and budget needs. You can train and deploy models faster and easier without provisioning or managing your resources. It also leverages Google’s cutting-edge research and innovation in AI, such as generative AI, to help you create state-of-the-art applications with minimal effort.
- Scalability:It scales the ML workflow by providing end-to-end MLOps tools that help you automate and standardize your ML projects throughout the development lifecycle. You can use it to efficiently manage, monitor, and govern your ML models at scale, reducing the work needed to maintain model performance in production and enabling your teams to focus on innovation.
- Security: Vertex AI secures the ML workflow by providing enterprise-grade security and compliance features that protect your data and models. You can use it to encrypt your data at rest and in transit, control access to your resources using identity and access management (IAM) policies, audit your activities using logging and auditing features and comply with various regulations and standards, such as GDPR, HIPAA, FedRAMP, and more.
How to get started with Vertex AI?
To get started with it, you must have a Google Cloud account and a project with billing enabled. You can sign up for a free trial and get $300 in free credits on Google Cloud services, including Vertex AI.
Once you have a Google Cloud account and project, you can follow these steps to start using it:
- Enable the Vertex AI API for your project.
- Create a Cloud Storage bucket to store your data and model artifacts.
- Choose an interface to interact with it, such as the Google Cloud Console, the gcloud command line tool, the Vertex AI SDK for Python, or client libraries.
- Choose a training method for your model, such as AutoML or custom training.
- Choose a deployment method to deploy your model, such as online or batch prediction.
- Choose a monitoring method for your model, such as logging or explainability.
You can find more detailed instructions and tutorials on how to use it in the documentation.
Where to find more resources and information?
If you want to learn more about Vertex AI and how it can help you create amazing AI applications, you can check out these resources:
Vertex AI is Google’s generative AI platform that simplifies and accelerates the machine learning workflow. IT provides a unified data and AI platform that lets you build, train, and scale ML models faster and easier, with fully managed tools for any use case. Whether a beginner or an expert, Vertex AI can help you create high-quality AI applications with minimal effort and cost.
In this article, we have explored Vertex AI, how it works, and what benefits it offers developers and businesses. We have also looked at some of the key features and capabilities of it, such as generative AI, AutoML, custom training, MLOps, and data integration. Finally, we have shown you how to get started with it and where to find more resources and information to learn and explore more about this amazing platform.
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Frequently Asked Questions – FAQs
1. What is Vertex AI?
Vertex AI is Google’s generative AI platform that simplifies the machine learning workflow by providing a unified data and AI platform for building, training, and scaling ML models with fully managed tools.
2. How does Vertex AI work?
It provides a comprehensive set of tools and services covering the entire ML lifecycle, including data labeling, datasets management, autoML, custom training, pipelines, experiments, model registry, prediction, monitoring, and more.
3. What are the benefits of Vertex AI?
It offers simplicity, speed, scalability, and security to developers and businesses. It integrates all the necessary tools, provides optimized infrastructure, automates workflows, and ensures enterprise-grade security and compliance.
4. How can I get started with Vertex AI?
To get started with it, you need a Google Cloud account and a project with billing enabled. Enable the Vertex AI API, create a Cloud Storage bucket, choose an interface, training and deployment methods, and monitoring options.
5. Where can I find more resources and information about Vertex AI?
You can find more resources and information on the it website, documentation, blog, videos, and courses provided by Google Cloud.
6. What are some key features of Vertex AI?
It offers generative AI, AutoML, custom training, MLOps, and data integration capabilities. It supports various ML frameworks and languages, provides a Jupyter notebook-based development environment, and offers pre-trained models in the Model Garden.