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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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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RAG vs Model tuning vs Large prompt window
Take a dive into the three primary methods for integrating your data into AI applications: prompts with long context windows, Retrieval Augmented Generation (RAG), and model tuning.
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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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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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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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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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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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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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Looker's Chart Config Editor & Visualization Assistant
Customized Looker charts → goo.gle/3YtK9NB Gemini in Looker → goo.gle/3Amc9uk Chart Config Editor → goo.gle/3AhvUDr Looker introduces two powerful features that
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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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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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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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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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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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GenAI is a game changer for podcasts 💡🎧
Gemini is revolutionizing how I learn from tech podcasts! It helps me pinpoint the exact discussions that align with my interests, so I can deep-dive into the topics that matter most.
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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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Demystifying RAG for developers
Developers can build better AI with Retrieval Augmented Generation (RAG) but what exactly is it?
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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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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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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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Embeddings for AI/ML and RAG apps
Are you a developer wondering how to use embeddings for your next machine learning project?
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Build RAG apps with embeddings
Embeddings crash course → goo.gle/4h6f6zL Embeddings are a key component of many machine learning applications.
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How to secure your cloud with VPC Service Controls
Overview of VPC Service Controls → goo.gle/405HhIT How you can mitigate data exfiltration with VPC Service Controls → goo.gle/3zY4xhA Discover how to strengthen your Google Cloud
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Leverage LLM strengths for your features 💪
Where do YOU see the biggest potential for LLMs in app development? I'm exploring this at a GenAI Hackathon and brainstorming ideas. Let's discuss!
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Deploy Hugging Face models from Vertex AI Model Garden
Model Garden → goo.gle/3zXNQ5P Explore AI models in Model Garden → goo.gle/3Yn4zJ2 Hugging Face models → goo.gle/400RczH You can deploy thousands of AI models from the
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Introducing pipe syntax in BigQuery and Cloud Logging
BigQuery pipe syntax documentation → goo.gle/4dFaZrB Pipe syntax examples and reference guide → goo.gle/3U1wcF3 Blog: Introducing pipe syntax in BigQuery and Cloud Logging →
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Experience multimodal AI with manga ONE PIECE (ワンピース)
Check out the Vertex AI Vector Search docs → goo.gle/3TS2l1H Check out the multimodal embeddings docs → goo.gle/3TPXgH7 Become an innovator → goo.gle/3Q9eEES Follow along as
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Gemini for Developers - Vertex AI
Get Started with Vertex AI Studio → goo.gle/3MXkjMt Join the Google Cloud Innovators Program → goo.gle/3TD3vy0 Want to learn more about how you can use Gemini with Vertex AI?
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Architecting a healthcare and life sciences startup with Google Cloud
Here to bring you the latest news in the startup program by Google Cloud is Daniela Serpa and Luis Sosa!
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