Google Vertex AI: Accelerating AI Development & More

Updated on August 14, 2024

The Vertex AI platform is Google’s new offering for AI developers. It has APIs for most foundational models and integrated machine learning workflows that you can leverage to build generative AI applications and chatbots. 

Google Vertex AI also offers developers access to premium Gemini and Gemma models and the products that make them possible, including PaLM 2, Imagen, and Cody. So, what can you do with Vertex AI? Let’s start by looking at its infrastructure.

Google Vertex AI Architecture

Google Vertex AI has many use cases, from building basic machine-learning workflows to deploying business-ready solutions. This is possible because of the multi-faceted architecture of the platform, which includes:

Business Solutions

1. Contact Center AI – Google hosts a contact center AI powered by Dialogflox CX. These models automate customer service and have natural interactions with customers. This is an all-in-one solution that provides customer support and captures insights from the interactions.
2. Document AI – Document AI lets you process data and access insights quickly. It’s based on the latest Generative AI models and allows you to classify your documents and provide structure to them using state-of-the-art machine-learning methods. This will enable you to get quick insights into your papers using semantic search. You can try it out here. 
3. Risk AI – Risk AI is a model that analyzes data to predict risks. This has use cases across industries and is used to understand things like credit risk, money laundering risks, etc. Several financial institutions, including the global bank HSBC, already use it for risk management in their financial products.

Development Solutions

1. Semantic Search Engine – Google has specific MLOps workflows that let you incorporate semantic search into your product. These workflows vectorize your data and help you provide an enhanced search experience to your customers.
   Additionally, Google can help you with grounding (improving the accuracy of your Generative AI results) and RAG (Retrieval Augmented Generation) frameworks. 
2. Conversation Engine – You can build no-code chatbots with Vertex AI. This process follows similar pathways as Dialogflow ES and Dialogflow CX and is powered by the latest generative AI chatbots from Google. It also includes integration with Langchain and Firebase for quick development and is enabled with the latest evaluation tools and metrics so that you can test the chatbots you deploy. 

AI Practitioner Solutions

1. AI Platforms – Google Vertex AI has everything you need to build and deploy AI models. With these AI platforms, you can:

• Ingest data from various sources – Documents, websites, etc.
• Analyze data using AI models.
• Train your model for a specific task.
• Fine-tune your model using question-answer pairs.
• Deploy your model to different places.
• Connect your model with results from Google search.
• Set custom instructions to train a model for specific tasks.
• Reduce the size of your model and make it run faster.
• Evaluate your model by using the latest tests.
• Increase the accuracy of your models by using RAG and Google search.

2. Model Playground – Google Vertex AI gives you access to 150+ foundational models you can play around with.  This includes

• The Gemini models
• The lightweight Gemma models
• Claude
• Imagen
• Cody
• Chirp
• BERT
• TII’s Falcon
• Other NLP models

This architecture allows Google to become an end-to-end solution for everyone, from AI researchers to business operators. Additionally, Google has built Vertex AI over its excellent cloud architecture, allowing developers to go and train their products without using external services or servers. 

Additionally, it has some key features that make it an excellent platform for developers. Let’s take a look. 

Features of Vertex AI

Since we’ve already outlined the platform’s architecture, we’ll focus on some key features in this section. The following features are helpful for developers who want to build generative AI applications using Google Vertex AI.

Vertex AI Notebooks

The Google Vertex AI Workbench is a Jupyter Notebook-like solution that lets you access all of the APIs in your workflow in one place. So, you can use this environment to code, train, and deploy your applications. 

These notebooks are integrated with Google BigQuery, and you can access data using the function. The notebooks work by letting you create instances that you can use in your machine learning workflows within the larger Vertex AI framework. 

Unified Machine Learning Workflows

Tools like AutoML, Explainable, VIzier, and Edge Manager are all available within the Google Vertex AI environment. These tools let you create and manage machine-learning workflows faster and more efficiently. 

MLOps Tools

Google Vertex AI also has several machine-learning operations tools to help you automate your workflows. This includes:

1. Evaluation Tools – To test and choose the best AI model for a particular task.
2. Model Registry – A centralized resource that lets you track and organize your ML models. You can use this registry to deploy, share, and reuse different models in your workflows. 
3. Feature Management – Previously called Feature Store, this repository allows you to take features from raw data and transform them into shareable attributes. These attributes can then be used in other ML models, making it easier to train new models. 

Model and Endpoint Resources

You can create your AI models within the Vertex workflow and deploy them at an Endpoint. Using the Endpoint within the Vertex workflow lets you generate predictions from your models. 

This lets you manage larger workflows where your AI model is a component without leaving the environment. You can also add your externally trained models to an endpoint for predictions. 

Duet AI

Duet AI is already available in the Google Workspace. In the Vertex AI environment, Duet acts as a copilot. This allows you to pull relevant data and inspect your codespaces as you work on building your applications. 

Like GitHub copilot, Duet can also act as a coding assistant and generate code or build small applications.

Enterprise-Grade Security

Google is a frontrunner in AI safety measures and ensures that all Cloud data is protected. This allows businesses to use and deploy their applications using Vertex AI without fearing data mishandling or privacy violations. 

Furthermore, Google was the first company to publish a comprehensive “AI and ML Privacy Commitment,” reaffirming its dedication to protecting consumer data.

Now that we know why Vertex AI is crucial for accelerating AI development let’s understand how enterprises worldwide use it.

Real-Life Applications of Google Vertex AI

Since Google has widespread reach, many enterprise customers have adopted Vertex AI. Some use cases include:

1. Optimizing Search Recommendations – Etsy uses the platform to provide its customers with an enhanced semantic search experience.
2. Transforming Document Processes – Bristol Myers Squibb uses Vertex AI to manage clinical trial data. The company uses the platform to streamline clinical trial documentation.
3. Video Editing – Canva uses the Vertex AI platform to enable AI-augmented video editing for its users. 
4. Climate-Risk Management – MSCI uses the platform to manage asset data for its clients and provide global insights into climate change risks.

Google has showcased multi-pronged use cases of the platform during their Next 2024 conference. You can access more real-life use cases for Vertex AI here.

Parting Words

To support the widely changing world of generative AI, Google has unveiled an end-to-end AI infrastructure platform with Vertex AI. This platform provides businesses with tools that they can use to manage and deploy AI applications. 

Plus, it integrates with GCP and provides developers with compute and ML resources to build the next generation of AI products. With full support for ML workflows and widespread adoption across different industries, Vertex AI has become famous for indie developers and enterprises. 

Google has been a forerunner in the generative AI race, and if you want to build an ML application, Vertex AI provides a great choice. However, if you want a no-code method to make customer support chatbots with Google’s infrastructure, Kommunicate lets you do it in simple steps with our no-code Dialogflow integrations.