Analyze Customer Reviews with Gemini Using Python Notebooks GSP1249 скачать в хорошем качестве

Analyze Customer Reviews with Gemini Using Python Notebooks GSP1249

Overview In this lab you learn how to extract keywords and assess customer sentiment in customer reviews using BigQuery Machine Learning with remote models (Gemini Pro). BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. One of its key features is BigQuery Machine Learning, which lets you create and run machine learning (ML) models by using SQL queries or with Colab Enterprise notebooks. Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the Gemini Pro, Gemini Pro Vision, and Gemini Flash models. At the end of this lab you will build a Python-based customer service application in a Colab Enterprise notebook within BigQuery, using the Gemini Flash model to respond to audio-based customer reviews. Objectives In this lab, you learn how to: Create a Python notebook in BigQuery using Colab Enterprise. Create a Cloud Resource connection in BigQuery. Create the dataset and tables in BigQuery. Create the Gemini remote models in BigQuery. Prompt Gemini to analyze keywords and setiment (positive, or negative) for text based customer reviews. Generate a report with a count of positive, and negative reviews. Respond to customer reviews at scale. Create an application for customer service representatives to respond to audio based customer reviews. #gcp #googlecloud #qwiklabs #learntoearn