An Introduction to Vertex AI

What is Vertex AI used for?

Google Cloud's Vertex AI is an immensely beneficial platform for developers, data scientists, and researchers to build, deploy, and manage ML models. These powerful models can make predictions from data, which helps companies better understand their customers.

The platform offers a commendable set of comprehensive features to capture the entire ML workflow. It is a common platform where data engineering, data science, and ML engineering teams can collaborate and scale business applications. Developers can use the already existing pre-trained models to build their custom models. This allows them to bring their ideas to life by seamlessly developing models tailored to specific applications, such as summarising, translating, and converting images to text.

There are several options for model training and deployment on the Vertex AI platform. For instance, AutoML allows for training models on various kinds of data, such as tabular, textual, images, or videos, without going through the tedious process of writing the code from scratch. The significant advantage of such a tool at your convenience is that it allows you to choose your preferred training process and hyperparameter tuning options. We’ll cover Vertex AI’s architecture and its features in the third section.

The Architecture of Vertex AI

Vertex AI's architecture is built on top of Google Cloud's GPUs, TPUs, storage, databases, and serverless platforms. This robust infrastructure enables end-to-end machine learning workflow in Vertex AI. Following is a description of its architecture, consisting of its key components:

• Model Garden: It's a collection of pre-built models and tools that help speed up model development. The models within the model garden can efficiently train large datasets.
• Open and integrated AI platform: Using the Vertex AI platform's training and tuning tools, deploying ML models has become easier and faster. For instance, Vertex AI notebooks are integrated with BigQuery, which enables a single surface across all data and AI workloads.
• AI platform extensions: This allows developers to link trained models with real-time data from enterprise apps and APIs. Extensions help improve model performance by connecting models to APIs for data ingestion and feature extraction.
• MLOps for predictive and generative AI: The platform provides purpose-built MLOps tools for automating, standardising, and managing ML projects. The following features are available for machine learning engineers to improve models throughout the life cycle:
  1. Vertex AI Evaluation: Helps identify the best model for a use case
  2. Vertex AI Pipelines: Helps orchestrate workflows
  3. Model registry: Helps to manage models
  4. Feature store: Helps with sharing and reusing ML features
• Vertex AIAgent Builder: This is a no-code agent builder console adjacent to grounding, orchestration, and customisation capabilities. It helps developers create generative AI applications grounded in their custom data.
• AI Solutions: Business users can build on top of the Vertex AI platform by using its set of AI solutions, such as Contact Centre AI, Document AI, Anti Money Laundering AI, and Discovery AI. These are practical solutions that provide targeted capabilities for businesses.

The Vertex AI platform allows AI practitioners and developers to create two types of AI applications: Search-based apps and Conversation-based apps. Let's take a close look at both of these features:

1. Search in Vertex AI: It supports data scientists with end-to-end search applications. Built-in capabilities include query understanding, ranking, and entity extraction. Other relevant features developers can customise include query suggestions, spelling correction, and query rewriting.
2. Conversation in Vertex AI: It facilitates building AI-powered conversational interfaces and chatbots. Vertex AI's training pipelines can be used to train conversation models, which can then be deployed as endpoints for the real-time interface.

What distinguishes Vertex AI from other Google AI models?

Vertex AI is a platform provided by Google Cloud that facilitates the building and deploying of ML models. It comprises valuable tools for data scientists and developers who want to build custom ML models for targeted business applications. It differs from other Google products and models, such as Bard and Gemini, as large language models (LLMs) provide human-like text responses and complete tasks based on prompts. For a clear distinction between the two, refer to the table below: 

Vertex AI and Google's AI models are power tools for their targeted audience and are extremely good at what they are designed to do. While Vertex AI has proven helpful for companies and startups to build and deploy custom ML models, Google's Gemini and Bard have made it seamless for end-users to interact with AI and incorporate it into their daily lives.

Vertex AI Access Guide for Companies

Vertex AI is easy to access. Following is a simplified walkthrough of getting started with the platform:

1. Signing up for Google Cloud: You'll need a Google Cloud account, which you can create by signing up on the website here: https://cloud.google.com/?hl=en. The account provides access to all Google Cloud products. You can try out 300 free credits over 90 days.

2. Activating the Vertex AI API: You can enable the Vertex API for your project by navigating to the API & Services dashboard.

3. Accessing Vertex AI in the Console: The Vertex AI section in the Google Cloud console provides options for datasets, models, endpoints, and more.

4. Exploring tutorials and documentation: The platform provides tutorials and documentation to help you navigate and build on it.

Once these steps are in place, you can start preparing to train your model using AutoML or a custom-trained model. You can then deploy your model and use it to make predictions. Vertex AI tools help you to monitor your models and continuously refine them.

A note on Vertex AI Pricing. The platform offers both free and paid versions. New signed-up users get some free credits. Additionally, you can access certain features for free but need a subscription to access premium features. The pricing structure depends on what you use Vertex AI for and correlates with the amount of data being used, stored, or processed.

Advantages and Challenges of Vertex AI

We’ve so far uncovered what Vertex AI offers, its features, and its usage guide. In this section, we will first enlist the many advantages that the platform offers, followed by a list of its main challenges. A review of its advantages and challenges will help you evaluate its fit for your organisation.

Advantages

• Unified platform: It is a unified platform which integrates several functions into a common platform. It provides ML workflow components such as data preparation, model training, and deployment. This helps reduce complexity and increases collaboration.
• Availability of open-source models: The accessibility of open-source models on the platform reduces friction and increases productivity. This helps developers and AI practitioners to scale projects readily.
• Ease of use: The models available on Vertex AI are not complicated to use, which makes it easy for developers to customise a solution to meet their business needs.
• Structured infrastructure: The platform is scalable, cost-effective, and provides easy management of huge data clusters.
• Coherent data-to-AI integration: Vertex AI's open-source frameworks and models and the availability of extensions allow for seamless integration and deployment within applications.

Challenges

• Ensuring data privacy: It is challenging for companies to transfer their data to Vertex AI's cloud infrastructure. Secure data transfer still leaves data processing and storage under scrutiny due to external threats such as cyber-attacks and unauthorised access.
• Preventing model bias: Even though Vertex AI is a comprehensive platform which provides the tools you need to bring your generative AI ideas of your business to life, it is still your responsibility to ensure that the models are unbiased by preventing training models on biased data.
• Minimising costs: Using Vertex AI can involve significant costs, especially if your business needs the platform for large-scale projects. It is challenging to allocate expenses effectively while ensuring optimised performance.

Conclusion

Vertex AI is benefiting companies in many ways. It is helping them offer generative AI applications by supporting developers with ML workflows. It's especially helpful for companies with less funding or those that are just starting out by offering a library of pre-trained models. Its features allow them to develop custom models using a variety of frameworks and languages. It's playing a huge role in AI-driven innovation and it's exciting to see what the future holds.