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Text Generation for Applications using Gen AI Studio

Explore the use of text generation models using Gen AI Studio on Vertex AI.

Generative AI is being used to develop new products and services across multiple industries, such as personalised marketing communications, chatbots for interacting with customers, and virtual assistants. For example, it can also be used to create chatbots that can answer customer questions and provide customer support.

In this course, you will explore the use of text generation models using Gen AI Studio on Vertex AI and learn how to incorporate those models into your application using the PaLM API and client libraries. You will learn how to design and tune prompts to ensure the best outputs for your applications and discuss how to fine-tune foundational models to improve model output quality.

GK# 899016
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Is This The Right Course?

  • Programming in Python
  • Leveraging APIs in applications
  • Basic familiarity with Google Cloud and Vertex AI as covered in the "Google Cloud Big Data and Machine Learning Fundamentals" course

Who Should Attend?

Application developers leverage Generative AI in their applications and machine learning practitioners supporting the development of GenAI-powered applications.

What You'll Learn
  • Understand Vertex AI generative AI options for your applications
  • Explore Gen AI Studio to interact with foundation models
  • Design and tune prompts for your Generative AI use cases
  • Implement the PaLM API into your applications using the Python SDK
  • Fine-tune foundation model weights to improve model output quality

Course Outline

Module 1: Generative AI on Vertex AI

  • Vertex AI on Google Cloud
  • Generative AI Options on Google Cloud
  • Introduction to the Course Use Case (Text Generation)

Module 2: Gen AI Studio

  • Introduction to GenAI Studio
  • Available models and use cases
  • Designing and testing prompts in the Cloud Console
  • Data governance in GenAI Studio

Module 3: Prompt Design

  • Why is prompt design so important?
  • Zero-shot vs. few-shot prompting
  • Providing additional context and instruction-tuning
  • Best practices

Module 4: Implementing the PaLM API

  • Lab: Getting Started with the Vertex AI PaLM API & Python SDK
  • Introduction to the PaLM API
  • Utilising generative models using the Python SDK
  • Understanding model parameters for text generation

Module 5: Fine-tuning Models

  • Scenarios to use model tuning
  • Workflow for model tuning
  • Preparing your model tuning dataset
  • Create a model tuning job
  • Loading a tuned model
  • Demo: Fine-tuning models for your specific use case
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Labs Outline
  • Lab: Getting started with Vertex AI Gen AI Studio's User Interface
  • Lab: Question Answering with Generative Models on Vertex AI
  • Lab: Use the PaLM API to integrate GenAI into Applications
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Prerequisites
  • Programming in Python
  • Leveraging APIs in applications
  • Basic familiarity with Google Cloud and Vertex AI as covered in the "Google Cloud Big Data and Machine Learning Fundamentals" course