Google Vertex AI: Everything You Need to Know What is Vertex AI, what you can do with it, and how to get started. Dive in.

By Hammad Syed in API

April 17, 2024 11 min read
Google Vertex AI: Everything You Need to Know

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The landscape of artificial intelligence (AI) and machine learning (ML) changes almost everyday, and Google Cloud’s Vertex AI is the latest sprout. Let’s dive in what Google Vertex AI is all about.

What is Google Cloud Vertex AI?

Google Cloud Vertex AI is sort of like a Swiss Army knife for AI and machine learning projects on Google Cloud. It’s a platform that bundles up a bunch of tools and services you need to get your AI models from the drawing board into the real world, without having to juggle a dozen different tools or write a boatload of glue code.

Whether you’re training a new model to recognize images, deploying a chatbot, or just experimenting with the latest in generative AI, Vertex AI can do it all.

Vertex AI simplifies the whole process, making it easier to build, test, deploy, and manage AI models. Plus, it’s integrated with other Google Cloud services, so if you’re already using things like BigQuery for data analytics or Cloud Storage for holding your datasets, it fits into your workflow pretty seamlessly.

You can also work with pre-trained models to save time, or train your own custom models if you’ve got specific needs. In short, Google Cloud Vertex is all about making AI less of a headache and more of a superpower for developers and data scientists.

Vertext AI also offers a suite of tools and functionalities, Vertex AI is engineered to accelerate the deployment and scaling of AI applications, from generative AI models to custom machine learning models.

Vertex AI Features

Vertex AI is built on the robust infrastructure of Google Cloud Platform (GCP), ensuring seamless integration with cloud services such as BigQuery for data warehousing, AI Platform for model training and deployment, and Cloud Storage for managing datasets. Its core proposition is to simplify the AI development workflow, enabling users to build, deploy, and manage ML models at scale with minimal effort.

  1. Generative AI and Foundation Models: Vertex AI merges the potential of generative AI, offering tools like the widely acclaimed Gemini model and its enhanced version, Gemini 1.5 Pro. These models facilitate the creation of content, code, and more, pushing the boundaries of what AI can achieve. Foundation models like BERT for natural language processing and Imagen for generating high-quality images underscore the platform’s versatility.
  2. Machine Learning Models and MLOps: The platform supports both AutoML for automatic model training and the ability for users to bring their custom models. With MLOps (ML operations) practices, Vertex AI ensures the life cycle management of ML models is streamlined and efficient, from deployment to monitoring and updating models in production.
  3. AI-powered Applications: Vertex AI is designed to power a wide range of AI applications, from chatbots that use natural language understanding to predictive models that forecast business trends. Its API interfaces and Python SDK make it accessible to a broad audience, from data scientists working in Python to developers integrating AI features into apps.
  4. Data and Model Management: At its core, Vertex AI provides robust tools for managing datasets and ML models. It offers features like Vertex AI Datasets for data preparation and Vertex AI Model for model management. This includes the ability to train, evaluate, and deploy models, as well as to create endpoints for real-time or batch predictions.
  5. Integration and Expansion: Vertex AI’s integration with Google Cloud services like BigQuery for SQL queries and the GCP’s compute resources ensures that users can easily expand their projects within the Google ecosystem. Additionally, the platform supports open-source frameworks and provides a model garden for users to share and discover models, fostering a collaborative environment.
  6. Vertex AI Pipelines and Tutorials: For orchestrating complex AI workflows, Vertex AI Pipelines offer a managed service to automate the deployment of AI applications, ensuring efficient use of resources and reproducibility. Google also provides extensive tutorials and documentation to help users get started, learn best practices, and explore advanced features.

Explore: What’s the difference between Google Cloud AI vs Vertex AI?

Use Cases and Applications

Vertex AI’s versatility makes it suitable for a broad range of generative AI applications & AI agents.

Here’s a what you could potentially build with Vertex AI

  1. Custom ML Model Development: Tackle unique business challenges head-on by crafting, fine-tuning, and deploying tailor-made ML models. From forecasting customer attrition and enhancing supply chain efficiency to fraud detection, Vertex AI equips you with the necessary toolkit.
  2. NLP Applications: Harness Vertex AI to dissect and comprehend textual information for tasks such as sentiment analysis, identifying key entities, or converting languages. This tech is a game-changer for monitoring social media, analyzing customer feedback, or developing sophisticated chatbots.
  3. Visual Content Analysis: Deploy models capable of interpreting and classifying images or video content. Applications range from streamlining digital asset management through automated tagging to bolstering security with real-time video analysis.
  4. Tailored Recommendations: Utilize Vertex AI to predict user preferences and craft personalized suggestions for products, services, or content, significantly enhancing user engagement across e-commerce platforms, content repositories, or mobile applications.
  5. Explorations in Generative AI: Delve into generative AI models to forge new creations, be it artwork, text, or code, paving the way for inventive design, content generation automation, or the next hit video game.
  6. Assistance in Healthcare: Employ the right datasets to develop models that aid in diagnosing medical conditions from imagery, forecasting patient outcomes, or customizing treatment strategies, all in support of medical professionals.
  7. Predictive Maintenance for IoT: Apply ML to anticipate maintenance needs or potential failures in industrial machinery or IoT setups, cutting costs and downtime in manufacturing and beyond.
  8. Financial Analytics Models: Create predictive models for risk evaluation, credit scoring, algorithmic trading, or market insights, empowering financial institutions to base decisions on solid data.
  9. Enhanced AI Customer Service: Build intelligent chatbots and virtual assistants that manage inquiries, offer customized support, and streamline customer service operations.
  10. Environmental Insight through AI: Apply AI to satellite imagery analysis for detecting environmental changes, tracking wildlife, or forecasting climatic shifts, contributing to ecological conservation efforts.

How to get started with Google Vertex AI

Getting started with Vertex AI is a pretty straightforward process, especially if you’re already familiar with Google Cloud. Here’s a casual walkthrough to kick things off:

Sign Up for Google Cloud

If you haven’t already, you’ll need to create a Google Cloud account. Head over to the Google Cloud website and sign up. You might get some free credits to play around with, which is always nice!

Activate the Vertex AI API

Once you’re logged into Google Cloud, navigate to the API & Services dashboard and enable the Vertex AI API for your project. This step is like turning on the engine before you drive.

Access Vertex AI in the Console

After enabling the API, go to the Vertex AI section in the Google Cloud Console. Here’s where the magic starts; you’ll see options for datasets, models, endpoints, and more.

Explore Tutorials and Documentation

Before diving in, it might be helpful to check out Google’s tutorials and documentation on Vertex AI. Google has put together some pretty neat resources that can help you grasp the basics and some advanced stuff, too.

Start a Project

Now for the fun part—starting your AI project. You can begin by creating a dataset. Vertex AI lets you import data from various sources, including Google Cloud Storage.

Train Your Model

Choose whether you want to use AutoML (great for beginners or if you want to save time) or a custom-trained model (if you’re feeling adventurous or have specific needs). Follow the steps to train your model with your dataset.

Deploy Your Model

After training, you’ll deploy your model to an endpoint. This step makes your model accessible for making predictions. It’s like putting your AI model behind the counter, ready to work.

Make Predictions:

Use the deployed model to make predictions. You can do this directly from the console or programmatically via the Vertex AI API. This is where you see your model in action!

Experiment and Iterate

AI and machine learning are all about experimentation. Play around with different models, datasets, and training parameters. Use Vertex AI’s tools to monitor your models and refine them for better performance.

Join the Community

Lastly, don’t forget to engage with the community. Google Cloud has a vibrant user community, and there’s a ton you can learn from forums, user groups, and events.

Google Vertex AI stands as a testament to Google’s commitment to advancing AI and ML technologies. By offering an integrated, scalable, and user-friendly platform, Vertex AI empowers developers and businesses to harness the power of AI.

Whether it’s through deploying high-quality ML models, leveraging generative AI for creative applications, or optimizing workflows with MLOps, Vertex AI provides the tools and infrastructure necessary to transform ideas into AI-powered solutions. With the backing of Google Cloud, Vertex AI is poised to be a driving force in the AI revolution, enabling innovation and efficiency across all sectors.

What is Vertex AI in Google?

Vertex AI is a comprehensive AI platform within Google Cloud designed to simplify the development, deployment, and scaling of AI and machine learning models, integrating various ML tools and services into a cohesive workflow.

Is Google Vertex AI free?

Google Vertex AI offers both free and paid services. Users can access certain features for free within the provided usage limits, but more extensive use or access to premium features requires payment.

How do I access Google Gemini AI?

To access Google Gemini AI, part of Vertex AI’s generative AI offerings, you need to use the Vertex AI platform through the Google Cloud Console, utilizing the appropriate APIs to interact with Gemini models.

What is the difference between GCP and Vertex AI?

GCP (Google Cloud Platform) is a broad suite of cloud computing services, including hosting, compute, storage, and more, while Vertex AI is a specific service within GCP focused on AI and machine learning development and deployment.

What are the options for training machine learning models in Google Cloud?

In Google Cloud, you can train machine learning models using AutoML for a no-code option, custom model training with AI Platform using your code, or TensorFlow, PyTorch, and other frameworks on Google Cloud’s compute resources.

What are some common use cases for machine learning in Google Cloud?

Common use cases include predictive analytics, natural language processing, chatbots, image recognition, and generative AI applications across various industries such as healthcare, finance, retail, and more.

What are the best practices for machine learning model development in Google Cloud?

Best practices include using MLOps for lifecycle management, leveraging pre-trained models when possible, experimenting with AutoML, ensuring data quality, and integrating Vertex AI’s end-to-end services for efficiency.

What is Vertex AI?

Vertex AI is Google Cloud’s AI and machine learning platform that enables data scientists and developers to build, deploy, and scale AI models more efficiently, offering tools for every stage of the AI model lifecycle.

What support is available for deploying machine learning models at scale in Google Cloud?

Google Cloud supports deploying machine learning models at scale through Vertex AI, providing managed services for training, deploying, and scaling models, with MLOps features to streamline operations.

What is GCP Vertex?

GCP Vertex, often referred to as Vertex AI, is a platform within Google Cloud that provides a unified environment for building, deploying, and scaling AI and machine learning models.

How does Vertex AI integrate with other Google Cloud services?

Vertex AI integrates seamlessly with Google Cloud services like BigQuery for data analysis, Cloud Storage for data management, and Cloud AI Platform for custom model training, enhancing its ML capabilities.

How do I start using Google Vertex AI for my machine learning project?

To start using Google Vertex AI, sign up or log into your Google Cloud account, navigate to the Vertex AI section in the Google Cloud Console, and follow the tutorials or documentation to begin your machine learning project.

What are large language models?

Large language models (LLMs) are AI systems trained on vast amounts of text data, enabling them to understand, generate, and interact with human language in complex and nuanced ways.
They leverage deep learning techniques to predict and produce text, making them versatile tools for a range of applications, including content creation, translation, and virtual assistance.
As technology evolves, these models become increasingly sophisticated, offering more accurate and contextually relevant linguistic interactions.

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Hammad Syed

Hammad Syed

Hammad Syed holds a Bachelor of Engineering - BE, Electrical, Electronics and Communications and is one of the leading voices in the AI voice revolution. He is the co-founder and CEO of PlayHT, now known as PlayAI.

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