Vertex AI: Rapid and enhanced innovation for companies

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• Google Vertex AI
• Vertex AI Benefits
• Vertex AI Features
  • Gemini
  • Access to API models and databases
  • Experimentation and model testing
  • Easily fit models with your own data
  • Connecting models to real-time data and actions
• Use Cases with Vertex AI
• Vertex AI Agent Builder

At a time when business innovations are evolving at a dizzying pace, AI has become an indispensable tool for companies looking to stay competitive.

However, developing and implementing AI solutions can be a complicated and confusing process. Vertex AI, a platform that offers comprehensive solutions for all types of industries, has emerged to make life easier for companies and developers. Here’s all the information!

Google Vertex AI

Vertex AI is an artificial intelligence platform developed by Google Cloud. It emerged as an evolution of Google’s previous AI services, such as AutoML and AI Platform, and aims to give developers and data scientists a more complete and easy-to-use solution.

It provides APIs for leading base models and tools to rapidly prototype, easily tune models with proprietary data, and seamlessly deploy them into applications.

It has a unified interface for developing, deploying, and managing AI models, as well as greater flexibility in the choice of frameworks and development tools, making it one of the most attractive platforms in the AI market.

Its main features are:

• Unified interface: Provides a unified interface for AI model development, deployment, and management, simplifying the process of model creation and production deployment.
• Compatibility: Supports a wide variety of frameworks and development tools, allowing users to use tools with which they are already familiar.
• Advanced AutoML: This incorporates advanced capabilities to facilitate the creation of AI models without requiring extensive knowledge in the field.
• Scalability and performance: It offers horizontal and vertical scalability to meet the needs of projects of any size, as well as optimized performance to accelerate model training and prediction time.

Vertex AI Benefits

As mentioned above, Vertex AI has become one of the most useful artificial intelligence solutions on the market, and using it, both for companies and developers, offers numerous advantages, such as:

• Increased productivity: The unified interface and advanced functionalities enable developers and data scientists to be more productive, accelerating the development cycle of AI models.
• Reduced costs: Thanks to its capabilities, the need for extensive AI expertise can be reduced, reducing the expense of hiring experts.
• Scalability and flexibility: Offering horizontal and vertical scalability, it allows companies to adapt to the changing needs of AI projects. In addition, support for different frameworks and tools provides flexibility in development.
• Improved decision-making: This helps improve decision-making by leveraging the power of AI to analyze large volumes of data and obtain valuable information.

Vertex AI Features

Gemini

Vertex AI provides access to Gemini’s multimodal models, which can understand (virtually) any input, combine different types of information, and generate almost any output. Instructions can be sent to Vertex AI with Gemini and tested using text, video, images, audio, or code.

With advanced reasoning and state-of-the-art generation capabilities, professionals can test sample instructions to extract text from images, convert text from images to JSON, or generate responses on uploaded images to compile next-generation AI applications.

Access to API models and databases

It allows you to choose the right model for each use case thanks to more than 40 proprietary models and more than 80 OSS and third-party models in Model Garden.

With access to Google’s base models as APIs, you can easily implement these models in applications.

In addition to Gemini, it also provides access to Gemma, a family of open, lightweight, state-of-the-art models created from the same research and technology used to create the Gemini models.

Experimentation and model testing

Tailor models to each use case with instruction design. It allows iterating on chat instructions and choosing from several ways to adjust responses.

Easily fit models with your own data

Improve the quality of model responses for each use case by fitting base models to your data with Vertex AI Studio.

Access state-of-the-art options, such as adaptor tuning and reinforcement learning with human feedback (RLHF) or style and subject tuning to generate images.

Connecting models to real-time data and actions

Vertex AI extensions provide a fully managed toolset for creating and managing extensions that connect models to proprietary data sources or third-party services.

Developers can then create generative AI applications that deliver real-time information, incorporate enterprise data, and take action on behalf of the user.

Use Cases with Vertex AI

This tool offers a wide variety of use cases for application in different industries. The most common examples are:

• Demand forecasting: Vertex AI can be used to predict demand for products or services, allowing you to optimize inventory and plan more efficiently.
• Text analytics: can be used to analyze large amounts of text, such as product reviews or customer feedback, and extract relevant information for decision making.
• Computer Vision: Companies can develop computer vision models for tasks such as image recognition, object detection, and motion tracking.
• Personalization: Personalized recommendation systems can be created that adapt to the tastes and preferences of each user.
• Task automation: The platform can be used to automate repetitive tasks, such as document classification or fraud detection, thus improving operational efficiency.
• Process optimization: It can be used to optimize internal processes, such as inventory management or delivery route planning, improving efficiency and reducing costs.

Vertex AI Agent Builder

The Vertex AI Agent Builder tool brings together all the tools necessary for developers to create enterprise-ready generative AI experiences, applications, and agents.

Some of its most powerful tools are components for retrieval-augmented generation (RAG) and the unique ability to ground Gemini outputs with Google Search.

Key features include:

• Codeless conversational agents: Enable you to design, deploy, and manage intelligent conversational artificial intelligence and process automation agents using natural language. Prompt-based agent creation tools can be combined with templates for rapid prototyping, experimentation, and deployment without the need to write extensive code.
• Increased presence in Google search: ensures accuracy and relevance of agents by connecting them to trusted data sources:
  • Gemini API to improve responses with Google Search results.
  • Vertex AI Search with RAG is ready to use and integrate enterprise data easily.
  • Options to create a custom RAG system with API for document processing, classification, data-driven response generation, and result validation.
  • Use of vector search to develop advanced applications based on vector embeddings.
  • Connectors for indexing enterprise application data.
  • Vertex AI extensions and function calling tools to allow agents to perform actions on behalf of users.
• Creating low-code AI applications: You can accelerate the development of AI-driven generative applications with a combination of low-code APIs and code-first orchestration. LangChaun is an open source library for creating custom GenAI applications to use VertexAI for modeling, tooling, and deployment. You can also enrich the LLM context with LlamaIndex and, with all of these, quickly experiment and create next-generation AI-driven experiences.
• Accelerate experimentation and deployment: This enables you to use comprehensive metrics and evaluation tools to assess the performance and quality of your generative AI applications and test them to fine-tune their behaviors and responses. This allows you to deploy generative AI applications in production environments effortlessly, ensuring scalability and reliability to meet business demands with Google Cloud’s enterprise-ready infrastructure.
• Security and compliance: A key pillar is to create AI experiences that meet enterprise standards and scalability needs. Vertex AI Builder offers security, compliance, and governance solutions that meet industry certifications and maintain data privacy and control over your applications.

One of the great advantages of generative AI is that its scalability occurs and transforms much faster than what we are used to.

The key to achieving rapid ROI and far-reaching transformation through GenAI is to focus on experimentation, scale, and security.

At Plain Concepts, we help you design your strategy, secure your environment, choose the best solutions, close technology and data gaps, and establish rigorous oversight that achieves accountable AI. You can achieve rapid productivity gains and build the foundation for new business models based on hyper-personalization or continuous access to relevant data and information.

We have a team of experts who have been successfully applying this technology in numerous projects, ensuring the security of customers. We have been bringing AI to our clients for more than 10 years, and now we propose a Framework for the adoption of generative AI:

• Unlock the potential of end-to-end generative AI.
• Accelerate your AI journey with our experts.
• Understand how your data should be structured and governed.
• Explore generative AI use cases that fit your goals.
• Create a tailored plan with realistic timelines and estimates.
• Build the patterns, processes, and teams you need.
• Deploy AI solutions to support your digital transformation.

Preparing your company to successfully adopt generative AI is the core of our framework, where we will cover 4 main pillars: strategy and governance of data and your privacy, security and compliance, reliability and sustainability, and responsible AI. This will help you avoid the risk of projects never making it to production.

Don’t wait any longer and start scaling your GenAI solutions!