# Vertex AI Published articles for Vertex AI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to use Vertex AI Prompt Optimizer with ground truth data DevFeed: [How to use Vertex AI Prompt Optimizer with ground truth data]() Original publisher: [Read original article]() Author: Alexander Nohe; Elena Erbiceanu Tener Published: 2026-01-20T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Prompt Engineering](), [Artificial Intelligence](), [Ground truth / benchmark quality](), [Google](), [CSV](), [data](), [Firebase]() Tags: [ai](), [cloud-storage](), [data](), [evaluation](), [firebase](), [format](), [gemini](), [google](), [google-cloud](), [how-to](), [learn](), [quality](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to use Vertex AI Prompt Optimizer with ground truth data to iteratively tune prompts and evaluate output quality. It describes preparing scripts and target descriptions in a Google Sheet, exporting the data as CSV, uploading it to Google Cloud Storage, and configuring optimization in a Colab Enterprise notebook. ### Source excerpt Learn how to use the Vertex AI Prompt Optimizer to automatically tune your prompts to get better results by iterating on your prompts and then running an evaluation on the outputs assessing their quality to see if it has improved. ## Improved Gemini audio models for powerful voice experiences DevFeed: [Improved Gemini audio models for powerful voice experiences]() Original publisher: [Read original article]() Author: Bibo Xu Published: 2025-12-12T17:50:50Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google](), [Google AI](), [voice ai](), [speech-to-speech](), [Artificial Intelligence](), [real-time](), [Streaming](), [Google Cloud Platform (GCP)]() Tags: [ai-studio](), [audio](), [customer-service](), [enterprise](), [gemini](), [google-cloud](), [none](), [real-time](), [speech](), [speech-to-speech](), [streaming](), [vertex-ai](), [voice]() ### AI overview Google describes upgrades to Gemini 2.5 Native Audio for live voice agents, including better function calling, instruction following, and multi-turn conversations. The model is available across Google AI Studio and Vertex AI, with rollouts to Gemini Live and Search Live. The article also introduces beta live speech-to-speech translation in Google Translate. ### Source excerpt An upgraded Gemini 2.5 Native Audio model across Google products and live speech translation in the Google Translate app. ## WeatherNext 2: Our most advanced weather forecasting model DevFeed: [WeatherNext 2: Our most advanced weather forecasting model]() Original publisher: [Read original article]() Author: The WeatherNext team Published: 2025-11-17T15:09:23Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google](), [model architecture](), [Inference](), [BigQuery](), [Google Cloud Platform (GCP)](), [AI Platform]() Tags: [ai](), [bigquery](), [google](), [inference](), [model](), [model-architecture](), [none](), [tpu](), [vertex-ai]() ### AI overview Google DeepMind and Google Research introduce WeatherNext 2, an AI weather forecasting model that generates hundreds of possible scenarios faster, at higher resolution, and with improved accuracy. Its forecast data is available through Earth Engine and BigQuery, with custom model inference offered through Vertex AI, and its technology is being integrated into several Google products. ### Source excerpt The new AI model delivers more efficient, more accurate and higher-resolution global weather predictions. ## Building for an Open Future - our new partnership with Google Cloud DevFeed: [Building for an Open Future - our new partnership with Google Cloud]() Original publisher: [Read original article]() Author: Jeff Boudier; Simon Pagezy Published: 2025-11-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Cloud](), [Open Source](), [Cloud Run](), [inference-endpoints](), [Low-Latency Inference](), [Cache](), [xet](), [data](), [Deployment]() Tags: [announcement](), [artificial-intelligence](), [cache](), [cloud](), [data](), [datasets](), [deployment](), [google](), [google-cloud](), [inference-endpoints](), [networking](), [open](), [open-source](), [partnership](), [partnerships](), [storage](), [vertex](), [vertex-ai](), [xet]() ### AI overview Hugging Face and Google Cloud announce a strategic partnership focused on making open AI models easier to use, customize, deploy, and govern. The article describes integrations across Vertex AI, GKE AI/ML, Cloud Run GPUs, and other Google Cloud infrastructure, plus a planned CDN Gateway using Hugging Face Xet and Google Cloud storage and networking to accelerate model and dataset downloads and improve supply-chain robustness. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI DevFeed: [Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI]() Original publisher: [Read original article]() Author: TAKAYUKI MIYANO Published: 2025-10-27T07:49:55Z Content type: article Language: en Sources: [Firebase Developers - Medium]() Topics: [App](), [Architecture & Design](), [Firebase](), [Flutter](), [Cloud Run](), [Cloud Functions](), [Retrieval Augmented Generation (RAG)](), [Messaging](), [Microservices](), [Security](), [Resilience](), [networking]() Tags: [architecture](), [cloud-functions](), [cloud-run](), [firebase](), [flutter](), [gemini](), [messenger](), [microservices](), [resilience](), [retrieval-augmented-generation](), [security](), [vertex-ai]() ### AI overview This article describes the architecture of Arc, a private messaging app built with Flutter and Firebase. It covers ephemeral messaging, mesh networking for resilience, Cloud Run microservices, Cloud Functions for event-driven processing, and a RAG architecture using Vertex AI Search and Gemini models. It also discusses security, privacy, and GenAI companions. ### Source excerpt Introduction: The Vision of Arc Our journey at Arc began with a singular vision: to redefine private communication in the digital age. We envisioned a world of ephemeral, intimate conversations -- a space where privacy isn't an afterthought, but the very foundation. This philosophy gave birth to Arc, a next-generation messaging app where every message, even in group chats, is designed to disappear over time. Our vision goes beyond just ephemerality. We also saw a critical need to build resilience, especially in regions with unstable internet infrastructure and in a world where disasters are a constant threat. This led us to develop our mesh networking technology, a potential lifeline when traditional communication fails. Our mission is to transform our app into a vital piece of social infrastructure that protects people's safety and gives them peace of mind. However, a vision of this scale demands an architecture that can compete on a global level. For us, true "optimization" is a comprehensive concept built on four pillars: Stability, Speed, Lightweight Design, and High Security. In this article, we'll explore the architectural decisions that shaped Arc. We'll detail how we integrated Flutter as our client, used Firebase's managed services as our foundation, and combined them with Cloud Run for scalable microservices, Cloud Functions for event-driven processing, and an advanced RAG (Retrieval-Augmented Generation) architecture using Vertex AI Search for grounding and Gemini models. This is the story of how we built a platform that is not only secure and high-performance but also intelligent and ready for future evolution. The Challenge: Going Beyond a Standard Messenger To make Arc a reality, we faced numerous technical hurdles that went beyond conventional architectures. Our goal wasn't just to build another messaging app; it was to create a new conversational experience and a resilient communication platform. Our app also includes GenAI companions (AI Characters) ## Full-text search and other searches with Data Connect DevFeed: [Full-text search and other searches with Data Connect]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-09-02T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Databases](), [Query (disambiguation)](), [AI search](), [Embeddings]() Tags: [database](), [embeddings](), [firebase](), [full-text-search](), [graphql](), [postgresql](), [search](), [sql-connect](), [vector](), [vector-search](), [vertex-ai]() ### AI overview Firebase Data Connect now supports PostgreSQL-powered full-text search. The article explains how it differs from string pattern filters and vector similarity search, using a movie app to show example use cases and query approaches. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Redpanda Cloud Summer 2025: Connect everywhere, Iceberg GA in cloud, and more DevFeed: [Redpanda Cloud Summer 2025: Connect everywhere, Iceberg GA in cloud, and more]() Original publisher: [Read original article]() Author: Mike Broberg Published: 2025-08-19T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Cloud](), [Streaming](), [Apache Iceberg](), [Serverless](), [Kafka](), [Cache](), [Google Cloud Platform (GCP)](), [HTTP](), [BigQuery](), [tbox](), [Slack]() Tags: [2026](), [announcement](), [apache-iceberg-integration](), [bigquery](), [byoc-on-azure](), [cloud](), [cloud-event-streaming-architecture](), [http](), [iceberg-ga-in-cloud](), [iceberg-topics-byoc](), [kafka](), [multi-cloud-solution-azure-aws-gcp](), [real-time-data-ingestion-lakehouse](), [redpanda-cloud-summer-2025](), [redpanda-connect](), [redpanda-connect-deployment](), [redpanda-serverless-features](), [serverless](), [serverless-capabilities-expansion](), [slack](), [stream-processing](), [streaming-pipelines-scalability](), [vertex-ai]() ### AI overview Redpanda Cloud's June-July 2025 summer update introduces general availability for Redpanda Connect across deployment types and supported cloud providers, expands Serverless capabilities, and makes Iceberg Topics generally available for AWS and GCP BYOC clusters. It also adds a Spanner change data capture connector, a write-optimized cache, Slack reaction output, and an HTTP gateway input for triggering streaming pipelines. ### Source excerpt Our summer update is sizzling with expanded Serverless capabilities, Iceberg topics on across all major clouds, and Redpanda Connect on all deployments. Read on. ## Grounding with Google Search in the Firebase AI Logic client SDKs DevFeed: [Grounding with Google Search in the Firebase AI Logic client SDKs]() Original publisher: [Read original article]() Author: Ankita Saxena; Daniel La Rocque Published: 2025-07-28T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Search](), [SDKs](), [Artificial Intelligence](), [Mobile](), [Web]() Tags: [ai](), [ai-logic](), [android](), [firebase](), [flutter](), [gemini](), [google](), [google-search](), [grounding](), [ios](), [javascript](), [mobile](), [news](), [react](), [sdks](), [search](), [tutorials](), [unity](), [updates](), [vertex-ai](), [web]() ### AI overview Firebase announces Grounding with Google Search for its AI Logic client-side SDKs. The feature lets Gemini search current web content before generating responses, enabling mobile and web applications to use more up-to-date information. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Fueld.AI's nutrition coach with Firebase and Google Cloud DevFeed: [Fueld.AI's nutrition coach with Firebase and Google Cloud]() Original publisher: [Read original article]() Author: Raïssa Tona Published: 2025-06-25T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Cloud Platform (GCP)](), [Artificial Intelligence](), [Cloud Functions](), [Firestore](), [Genkit]() Tags: [ai](), [authentication](), [case-study](), [cloud-functions](), [cloud-logging](), [cloud-storage](), [firebase](), [firestore](), [gemini](), [genkit](), [google-analytics](), [google-cloud](), [hosting](), [news](), [vertex-ai]() ### AI overview Fueld.AI used Firebase and Google Cloud services to build a health and nutrition app that analyzes meal photos, provides AI coaching, and securely shares data with health professionals. The article describes its use of Authentication, Cloud Storage, Cloud Functions, Genkit, Gemini, Cloud Firestore, Hosting, Analytics, and Cloud Logging. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new in Firebase at I/O 2025 DevFeed: [What's new in Firebase at I/O 2025]() Original publisher: [Read original article]() Author: Aja Hammerly; Seba Gnagnarella Published: 2025-05-20T10:51:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Google](), [Figma](), [Firestore](), [Authentication](), [ui]() Tags: [2025](), [ai](), [ai-logic](), [app-check](), [app-development](), [app-hosting](), [authentication](), [cloud](), [code](), [data](), [database](), [design](), [development](), [emulator-suite](), [figma](), [firebase](), [firebase-ai-logic](), [firebase-studio](), [firestore](), [gemini](), [genkit](), [google](), [google-i-o](), [launch](), [mcp](), [news](), [phone-number-verification](), [postgresql](), [remote-config](), [sql-connect](), [terraform](), [unity](), [updates](), [vertex-ai]() ### AI overview Firebase's I/O 2025 updates introduce new Firebase Studio capabilities, including Gemini 2.5 support, Figma design import, image replacement, and automated backend setup with Firebase Authentication and Firestore integrations. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Building AI-powered apps with Firebase AI Logic DevFeed: [Building AI-powered apps with Firebase AI Logic]() Original publisher: [Read original article]() Author: Thevi Sundaralingam; Dmitry Lyalin; Miguel Ramos Published: 2025-05-20T10:49:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Generative AI](), [SDKs](), [Genkit](), [API]() Tags: [ai](), [ai-logic](), [dashboards](), [firebase](), [firebase-ai-logic](), [gemini](), [genkit](), [google-i-o](), [hybrid-inference](), [inference](), [launch](), [node-js](), [on-device](), [on-device-ai](), [sdks](), [vertex-ai]() ### AI overview Firebase announces Firebase AI Logic, the evolution of Vertex AI in Firebase, with client-side and Genkit-based server-side integrations for generative AI. New capabilities include Gemini Developer API access, hybrid and on-device inference, Unity support, image generation and editing, enhanced observability, and dynamic model lookup in Genkit for Node.js. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Data Connect Pricing DevFeed: [Data Connect Pricing]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-04-22T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google](), [Cloud](), [PostgreSQL]() Tags: [billing](), [firebase](), [google-cloud](), [postgresql](), [pricing](), [sql-connect](), [updates](), [vertex-ai]() ### AI overview This Firebase blog post explains the pricing structure for Firebase Data Connect after general availability. It covers Data Connect service charges, Cloud SQL PostgreSQL costs, network egress, operation counts, and possible Vertex AI costs for vector embeddings. ### Source excerpt News, tutorials, and updates from the Firebase team. ## What's new in Firebase at Cloud Next 2025 DevFeed: [What's new in Firebase at Cloud Next 2025]() Original publisher: [Read original article]() Author: Kristen Richards; Andrea Wu Published: 2025-04-09T06:07:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Cloud](), [Genkit](), [Next.js](), [Testing]() Tags: [2025](), [ai](), [ai-logic](), [android](), [angular](), [app-distribution](), [app-hosting](), [cloud-next](), [firebase](), [firebase-studio](), [flaky](), [gemini](), [genkit](), [go](), [graphql](), [ios](), [launch](), [next-js](), [postgresql](), [python](), [react](), [react-native](), [sql-connect](), [updates](), [vertex-ai]() ### AI overview Firebase announces product updates at Cloud Next 2025, including Firebase Studio, an App Testing agent, expanded Genkit language support, Vertex AI integrations, and general availability for Data Connect and App Hosting. ### Source excerpt News, tutorials, and updates from the Firebase team. ## React Native Support and Gemini Live APIs for Vertex AI in Firebase DevFeed: [React Native Support and Gemini Live APIs for Vertex AI in Firebase]() Original publisher: [Read original article]() Author: Miguel Ramos Published: 2025-04-09T06:03:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [React Native](), [API](), [Streaming](), [Mobile](), [Flutter](), [Web]() Tags: [ai](), [ai-logic](), [android](), [apis](), [cloud-next](), [firebase](), [flutter](), [gemini](), [launch](), [react-native](), [streaming](), [updates](), [vertex-ai](), [web]() ### AI overview Firebase announces updates integrating Vertex AI, including Gemini Live API support for streaming audio and text interactions, React Native support, public preview SDKs for Android and Flutter, and Firebase SDK code snippets in Vertex AI Studio. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Firebase Data Connect is generally available DevFeed: [Firebase Data Connect is generally available]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-04-09T06:02:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Databases](), [GraphQL](), [SDKs](), [Retrieval-Augmented Generation]() Tags: [ai](), [android](), [angular](), [announce](), [cloud-next](), [firebase](), [gemini](), [graphql](), [ios](), [launch](), [postgresql](), [react](), [sdks](), [sql-connect](), [vertex-ai]() ### AI overview Firebase announces the general availability of Data Connect, a backend-as-a-service powered by Cloud SQL for Postgres. The release includes GraphQL-based schemas, generated secure endpoints and typesafe SDKs, relational queries, vector search and Vertex AI integration for RAG flows. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Add image generation to your apps with Imagen 3 DevFeed: [Add image generation to your apps with Imagen 3]() Original publisher: [Read original article]() Author: Miguel Ramos Published: 2025-03-03T04:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [AI Models](), [Generative AI](), [SDKs](), [Android](), [Flutter](), [iOS](), [Web]() Tags: [ai](), [ai-logic](), [ai-models](), [android](), [firebase](), [flutter](), [generation](), [generative-ai](), [image-editing](), [ios](), [launch](), [low-latency](), [sdks](), [vertex-ai](), [web]() ### AI overview Vertex AI in Firebase adds public preview support for Imagen 3 and Imagen 3 Fast, enabling image generation in Android, iOS, Flutter, and Web applications. The article describes text-to-image generation, resolution choices, text in images, watermarking, safety settings, and a limit of four images per request, while noting that image editing, image inputs, and predefined styles are not yet supported. ### Source excerpt Available in Vertex AI in Firebase (Public Preview) ## internal AI: A Genkit-Based Internal AI Chat Released as Open Source! DevFeed: [internal AI: A Genkit-Based Internal AI Chat Released as Open Source!]() Original publisher: [Read original article]() Author: tanabee Published: 2025-02-17T17:14:41Z Content type: article Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [AI Chat](), [Open Source](), [Firebase](), [Retrieval-Augmented Generation](), [Generative AI]() Tags: [emulator](), [firebase](), [firestore](), [function-calling](), [gemini](), [genkit](), [google-cloud-platform](), [llm](), [open-source](), [rag](), [vertex-ai]() ### AI overview This article introduces internal AI, an open-source Genkit-based internal chat implementation. It describes its usage-based pricing, customizable RAG and function-calling support, Firebase deployment, monitoring, domain restrictions, and local testing with the Firebase Emulator. ### Source excerpt internal AI: An open-source Genkit-based internal AI chat implementation I have released internal AI, an AI chat solution you can easily and cost-effectively run internally, under an open-source license! Recently, Firebase Genkit reached version 1.0 and became stable for production use. With Genkit, you can quickly build applications that leverage generative AI. However, Genkit itself does not include a user interface. As a use case for an internal AI chat, I decided to make internal AI available as open source. Because internal AI uses a pay-as-you-go model instead of charging per user, you can optimize costs by only paying for the amount of usage. Moreover, thanks to Genkit's extensibility, you can combine RAG (Retrieval-Augmented Generation) and Function Calling to tailor AI features to your organization's needs. This opens the door to more practical AI applications, such as specialized internal information retrieval or automated workflow processes! GitHub - tanabee/internal-ai internal AI -- ChatKey Features Open-source project, allowing customization to fit your organization's needs Abstracted interface with generative AI via Genkit Deployable on Firebase Firebase AI Monitoring for monitoring requests to generative AI Restricting Usage to Specific Domains Usage-based pricing, independent of the number of users Open-source project, allowing customization to fit your organization's needs internal AI is provided as a boilerplate, which you can freely fork and customize according to your organization's requirements. Genkit can be extended in many ways, such as using RAG for accessing internal data or utilizing Google Search for grounding. To keep things simple for initial adoption, this example just communicates with Gemini 2.0. const ai = genkit({ // ... model: gemini20Flash001, }) export const chatFlow = ai.defineFlow( // ... async (messages) => { const response = await ai.generate({ messages }) return response.messages }, ) internal-ai/functions/src/genkit/chatFlow.t ## Trippy Wins the Gemini API Developer Competition with a Firebase-Powered Travel App DevFeed: [Trippy Wins the Gemini API Developer Competition with a Firebase-Powered Travel App]() Original publisher: [Read original article]() Author: Lloyd Hightower Published: 2024-11-21T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [API](), [App](), [Cloud Functions](), [Firestore](), [Serverless](), [Authentication](), [Cloud](), [real-time]() Tags: [ai](), [app](), [authentication](), [backend](), [cloud-functions](), [cloud-messaging](), [cloud-storage](), [firebase](), [firestore](), [gemini](), [machine-learning](), [messaging](), [news](), [notifications](), [real-time](), [recognition](), [recommendations](), [serverless](), [storage](), [travel](), [vertex-ai]() ### AI overview Firebase announces Trippy as the winner of its Gemini API Developer Competition. The travel app combines Gemini with Firebase services to provide personalized itineraries, recommendations, multilingual support, games, receipt-based expense tracking, currency conversion, and contextual chat. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Data Connect Pricing DevFeed: [Data Connect Pricing]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2024-11-20T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [PostgreSQL](), [Cloud](), [Google]() Tags: [billing](), [cloud](), [firebase](), [google-cloud](), [postgresql](), [pricing](), [sql-connect](), [vertex-ai]() ### AI overview This Firebase post explains pricing for Data Connect during Public Preview and after General Availability. It covers the Data Connect service, the Cloud SQL for PostgreSQL instance, optional Vertex AI vector embeddings, network egress, and possible cross-region or direct Cloud SQL charges. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Learn to build and run AI powered apps at Firebase Demo Day '24 DevFeed: [Learn to build and run AI powered apps at Firebase Demo Day '24]() Original publisher: [Read original article]() Author: Yasmin Gehman Published: 2024-11-19T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Demo](), [Genkit](), [hosting]() Tags: [ai](), [app-hosting](), [build](), [demo](), [demo-day](), [event](), [firebase](), [full-stack-development](), [gemini](), [genkit](), [hosting](), [remote-config](), [vertex-ai]() ### AI overview Firebase Demo Day 2024 presents eight short demos showing how to build, deploy, run, monitor, and optimize AI-powered applications with Firebase products including Genkit, Vertex AI in Firebase, Gemini in Firebase, and Firebase App Hosting. ### Source excerpt See us transform our travel app with the power of Firebase and AI ## Automating Infrastructure as Code with Vertex AI DevFeed: [Automating Infrastructure as Code with Vertex AI]() Original publisher: [Read original article]() Published: 2024-11-05T22:23:09Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [Infrastructure as code](), [Artificial Intelligence](), [Generative AI](), [GitOps](), [Code](), [Google](), [Kubernetes]() Tags: [ai](), [apis](), [architecture](), [ci-cd](), [cloud](), [cloud-infrastructure](), [code](), [configuration](), [development](), [gcp](), [gemini](), [generative](), [generative-ai](), [iac](), [infrastructure](), [infrastructure-as-code](), [integration](), [konfigurate](), [llm](), [platform](), [product](), [use-cases](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to integrate Google Vertex AI with Gemini 1.5 into the Konfigurate platform to automate infrastructure-as-code creation. It covers multimodal input, context-aware and structured output, output tuning without model tuning, and model testing. ### Source excerpt A lot of companies are trying to figure out how AI can be used to improve their business. Most of them are struggling to not just implement AI, but to even find use cases that aren't contrived and actually add value to their customers. We recently discovered a compelling use case for AI integration in our Konfigurate platform, and we found that implementing generative AI doesn't require a great deal of complexity. I'm going to walk you through what we learned about integrating an AI assistant into our production system. There's a ton of noise out there about what you "need" to integrate AI into your product. The good news? You don't need much. The bad news? It took too much time sifting through nonsense to find what actually helps deliver value with AI. ## Vertex AI in Firebase is now Generally Available DevFeed: [Vertex AI in Firebase is now Generally Available]() Original publisher: [Read original article]() Author: Miguel Ramos Published: 2024-10-21T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google AI](), [SDKs](), [API](), [JSON](), [Mobile](), [multimodal](), [Web]() Tags: [ai](), [ai-logic](), [android](), [api](), [app-check](), [cloud-storage](), [firebase](), [flutter](), [function-calling](), [gemini](), [generative-ai](), [ios](), [javascript](), [json](), [launch](), [mobile](), [multimodal](), [remote-config](), [sdks](), [structured-output](), [vertex-ai](), [web]() ### AI overview Firebase announces the general availability of Vertex AI in Firebase, enabling mobile and web apps to access Gemini models through Firebase client SDKs. The release includes support for multimodal prompting, system instructions, function calling, and structured JSON output with schemas. ### Source excerpt Securely add generative AI features to your app using Firebase client SDKs ## Firebase Data Connect: now in public preview! DevFeed: [Firebase Data Connect: now in public preview!]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2024-10-09T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [PostgreSQL](), [GraphQL](), [SDK](), [Node.js](), [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence]() Tags: [admin-sdk](), [data](), [firebase](), [graphql](), [launch](), [node-js](), [postgresql](), [preview](), [retrieval-augmented-generation](), [sdk](), [sql-connect](), [updates](), [vertex-ai]() ### AI overview Firebase Data Connect is now available in Public Preview. The service uses a Cloud SQL Postgres database, GraphQL-based schemas, generated SDKs, schema migrations, semantic vector search, and Vertex AI integration for retrieval-augmented generation workflows. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI DevFeed: [Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI]() Original publisher: [Read original article]() Author: Alvaro Bartolome; Philipp Schmid; Simon Pagezy; Jeff Boudier Published: 2024-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [llama](), [Google Cloud Platform (GCP)](), [Inference](), [tgi](), [Meta](), [GPU](), [hugging face](), [Containers]() Tags: [container](), [cuda](), [gcp](), [google-cloud](), [gpu](), [hugging-face](), [inference](), [llama](), [nlp](), [partnerships](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to programmatically deploy the FP8-quantized Meta Llama 3.1 405B model on Google Cloud Vertex AI using Text Generation Inference and Hugging Face Deep Learning Containers. It covers deployment on an A3 node with eight NVIDIA H100 GPUs, alternative deployment paths, and the memory considerations for running the model. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. [Next page]()