Intro to AI agents
Vertex AI Agent Builder quickstart → goo.gle/3UPJ7dN GenAI powered App with Genkit → goo.gle/4fCSTrK Demystifying AI agents, Googlers Aja Hammerly and Jason Davenport provide a
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Advanced RAG techniques for developers
Advanced RAG Techniques→ goo.gle/4dQTxQP Combining Semantic & Keyword Search → goo.gle/3NuYQuz Task Type Embedding → goo.gle/3AfAlOS Unlock the full potential of Retrieval
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How to use Retrieval Augmented Generation (RAG)
Get familiar with RAG → goo.gle/3YclIUC What is RAG? → goo.gle/4hahoOi What is Retrieval Augmented Generation (RAG) and how does it enhance generative AI capabilities in apps?
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Google Axion Processors, explained
Learn about Google Axion Processors → g.co/cloud/axion Compete Google Compute Engine VMs → g.co/compute/compare Have you ever wondered how Arm is different from x86?
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AI + your code: Function Calling
Function Calling with Vertex Tutorial (Notebook) → goo.gle/4dGvBzz Function Calling with Gemini API → goo.gle/4eEOLXR Tool Calling with Genkit →
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Prompt engineering for developers
Are you using chatbots and receiving high quality responses? It may be your prompt that is affecting the chatbot’s response. Take your generative AI skills to the next level by improving your prompts.
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8.2
RAG with LangChain on Google Cloud
Discover how to enhance the accuracy of your AI applications using Retrieval-Augmented Generation (RAG).
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7.7
Semantic modeling for AI
Looker's semantic layer provides a single source of truth → goo.gle/3YHALpz Gemini in Looker overview → goo.gle/4fmgq0f Gemini in Looker announcement → goo.gle/3YYQNwF In
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What are Hugging Face Deep Learning Containers?
Hugging Face Deep Learning Containers docs → goo.gle/4dKMtoU Updates every month (release notes) → goo.gle/3YmYzPi Hugging Face Expert Support Program → goo.gle/3U9jVP1 The
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8.2
Data insights with Looker for Google Workspace
Looker Workspace Block → goo.gle/3U9UPQ9 Unleash AI insights with Looker and Workspace → goo.gle/4eIvS6i How Looker integrates with Google → goo.gle/3Uaij7s Create a data
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11.9
65K node Kubernetes AI Platform - A Reality
The size of generative AI models is constantly increasing, with current models reaching hundreds of billions of parameters and the most advanced ones approaching 2 trillion.
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9.3
Advanced RAG techniques for better retrieval performance
Unlock the full potential of Retrieval Augmented Generation (RAG) with advanced techniques or enhancing the quality of responses from large language models (LLMs).
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6.6
Function calling for LLMs, what is it? 🤔
Want to take your AI skills to the next level? Learn about "function calling" and unlock new possibilities for your code and external APIs.
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6.2
Architecting a RAG Podcast Summarizer #AI #Tech
For my team's GenAI hackathon, I decided to build a tool to help me go through all my podcasts more easily.
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9.1
Deploy open models with TGI on Cloud Run
Tutorial: How to deploy Gemma 2 on Cloud Run with TGI → goo.gle/3Yoztjh Get started with Cloud Run GPU → goo.gle/4ec7mJS Docs: Text Generation Inference → goo.gle/4e7qusz
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9.1
How to run anything on Google Axion Processors
There are 7 types of workloads developers can migrate to run on Arm-based CPUs.
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11.8
What is an AI agent?
Dive into the world of AI agents in the latest episode of Real Terms for AI with Googlers Aja Hammerly and Jason Davenport.
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7.5
Demystifying RAG for developers
Developers can build better AI with Retrieval Augmented Generation (RAG) but what exactly is it?
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Quick Gemma 2 deployment with Hugging Face
Tutorial: Serve Gemma on GKE with TGI → goo.gle/4fFKt2Q Learn more about TGI (text generation inference) from Hugging Face → goo.gle/4e7qusz Hugging Face Deep Learning containers for
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Deploy HUGS on GKE with Hugging Face
Getting started with HUGS on Google Cloud → goo.gle/3OhK8aL HUGS in the Google Cloud Marketplace → goo.gle/3ZbVPG6 Blog: Introducing HUGS → goo.gle/4eCs5Xl Deploy open
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Learn Hybrid Search with Vertex AI Vector Search
Notebook: Combining Semantic & Keyword Search: A Hybrid Search Tutorial with Vertex AI Vector Search → goo.gle/3YXW3zp Document: About hybrid search → goo.gle/40R9vrr Semantic
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