Vertex AI Search and Conversation with Enterprise Use Case.

What is Google Cloud Vertex AI

Google Cloud Vertex AI is a unified platform for building, deploying, and managing Machine Learning (ML) models and Artificial Intelligence (AI) applications on Google Cloud. It streamlines the entire ML lifecycle, encompassing data engineering, data science, and ML engineering workflows.

Here are some key features of Vertex AI:

• MLOps Tools: Vertex AI offers a suite of tools for automating, standardizing, and managing the ML project lifecycle. This includes functionalities like:
• Model Training and Deployment: Train various types of models, including custom models built with TensorFlow or PyTorch, and AutoML models for tasks where you might not have extensive ML expertise. Deploy these models for real-time predictions.
• Model Evaluation: Compare different models and identify the best performing one for your specific use case.
• Vertex AI Pipelines: Orchestrate complex ML workflows involving data preparation, training, evaluation, and deployment.
• Model Registry: Manage the entire lifecycle of your models, including versioning, deployment, and monitoring.
• Feature Store: Centralize and manage feature engineering for your ML projects, enabling sharing and reuse of features across models.

Generative AI for Search and Conversation:

Vertex AI offers functionalities to build and deploy generative AI applications for search and chat functionalities. This includes pre-built components and an orchestration layer to simplify development.

What is Search & Conversation in Vertex AI with use cases?

• Search: Vertex AI Search helps you create search applications that understand natural language queries. It can pull information from various sources, including structured data and documents, and use semantic understanding to find the most relevant results, even if the exact keywords aren’t used.
• Conversation: Vertex AI Conversation lets you build chatbots and virtual assistants that can have conversations with users. It can understand the intent behind a user’s message, extract important details, and respond accordingly. These conversations can even be used to facilitate actions, like making a payment within a chat window.

Use Cases:

• Enterprise Search: Search through a company’s internal documents, codebase, or knowledge base using natural language, making it easier for employees to find the information they need.
• Customer Service Chatbots: Build chatbots that can answer customer questions, troubleshoot problems, and even complete transactions, improving customer satisfaction and reducing support costs.
• Virtual Assistants: Create virtual assistants for employees that can answer questions, schedule meetings, and automate tasks, improving productivity.
• Conversational Interfaces: Develop chat interfaces for various applications, allowing users to interact with them in a more natural way.

Reference URL :

Cloud Technical Series AI Edition