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LinkedIn Learning

Google Gemini for Developers

via LinkedIn Learning

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Overview

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Learn best practices, patterns and processes for developers and DevOps teams who design and implement LLM-based applications using Google Gemini.

Syllabus

Introduction
  • Build LLM-based applications with Google Gemini
  • What you should know
  • Using cloud services
1. Gemini Dev Environments
  • Understanding Google Gemini
  • Use Google AI Studio
  • Use Vertex AI Studio
  • Use Colab Notebooks
  • Use Gemini Code Assist in cloud workstations
2. Gemini Prompts
  • Use Google AI Studio to test prompts
  • Use system instructions with prompts
  • Design and test language model prompts
  • Design and test multimodal prompts
  • Design prompts in Cloud Code for APIs
3. Gemini Notebooks and APIs
  • Using the Gemini API: Set up
  • Using the Gemini API: Testing prompts
  • Using function calling with Gemini
  • Programming multimodal use cases
  • Use the Gemini File API
  • Use embeddings with Gemini
  • Set up a RAG pattern with Gemini
  • Implement a RAG pattern with Gemini
4. Gemini Model Evaluation
  • Understand model grounding
  • Ground a model with Google Search
  • Ground with a semantic retriever
  • Understand model evaluation
  • Perform model evaluation
  • Fine-tune a Gemini model
5. Gemini Applications
  • Use Vertex AI Model Garden
  • Deploy a GenAI cloud architecture: Document summaries
  • Deploy a GenAI cloud architecture: Knowledge base
  • Preview of Vertex AI Agent Builder
Conclusion
  • Next steps

Taught by

Lynn Langit

Reviews

4.7 rating at LinkedIn Learning based on 57 ratings

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