Cloud Storage

Google Cloud's object storage (GCS). Used for model inputs/outputs, datasets, generated media, and grounding corpora. · Official docs

Related concepts

• Getting Started · Gemini Capabilities · Prompt Engineering · Function Calling & Tools · Agents & ADK · Agent Engine · RAG & Grounding · Vertex AI Search · Embeddings & Vector Search · Vision · Audio & Speech · Multimodal Live API · Image & Video Generation · Evaluation · Tuning & Customization · Open & Partner Models · Responsible AI · Translation · MLOps & Deployment · Applied Use Cases

Used in 175 notebooks

Agents & ADK (14)

• Building a Multimodal Trip Planner with ADK on Vertex AI Agent Engine Memory Bank — Builds and deploys a multimodal ADK trip planner using Vertex AI Agent Engine Memory Bank.
• Get started with A2A on Agent Engine — Builds, deploys, and queries an A2A Q&A agent on Vertex AI Agent Engine.
• Claude with ADK on Vertex AI Agent Engine — Builds and deploys a Claude-powered ADK Reddit agent on Vertex AI Agent Engine with memory and tracing.
• Deploy your first agent to Vertex AI Agent Engine — Deploys ADK agents to Vertex AI Agent Engine with Express Mode, agent object, and inline source workflows.
• Get started with Agent Engine Terraform Deployment — Deploy Vertex AI Agent Engine agents with Terraform, cloudpickle packaging, and ADK tools.
• Get started with Cloud API Registry on Vertex AI Agent Engine — Builds and deploys an ADK BigQuery data analyst agent using Cloud API Registry on Vertex AI Agent Engine.
• Get started with Code Execution on Vertex AI Agent Engine — Runs LLM-generated Python securely with Vertex AI Agent Engine Sandbox and ADK agents.
• Getting Started with Live API on Agent Engine — Deploys bidirectional streaming agents on Vertex AI Agent Engine using Gemini Live API audio and ADK tools.
• MCP on Vertex AI Agent Engine with custom installation scripts — Deploys a Reddit MCP tool agent to Vertex AI Agent Engine with custom install scripts.
• Building multi-agent systems with Vertex AI and Claude — Builds a Vertex AI Agent Engine multi-agent market analysis system with Gemini, Claude, ADK, A2A, and MCP.
• Building Multi-Agent Systems with Vertex AI and Llama model — Builds a traced Vertex AI multi-agent trading analyst with Gemini, Llama, ADK, A2A, and MCP tools.
• Intro to Managed Agents API on Agent Platform (cURL) — Shows how to manage and interact with Managed Agents on Agent Platform using REST and cURL.
• Intro to Managed Agents API on Agent Platform (Python) — Shows how to create, inspect, interact with, and delete Managed Agents with google-genai.
• Managed Agents API - Analyzing the 2026 World Cup — Deploys a Managed Agents API World Cup analyst agent with GCS skills and multi-turn interactions.

Audio (3)

• Get started with Chirp 3 Transcription — Uses Chirp 3 with Speech-to-Text V2 for sync, language-auto, diarized batch, and streaming transcription.
• Create a Multi-Speaker Podcast with Gemini 2.0 & Text-to-Speech — Generates a two-speaker podcast from a PDF using Gemini JSON output and Text-to-Speech MP3 synthesis.
• Narrate a Multi-character Story with Gemini and Text-to-Speech — Generates a Gemini play script and narrates it with distinct Text-to-Speech voices per character.

Embeddings & Vector Search (9)

• 🛡️ AI Brand Safety: Three-Tier Agent Anomaly Detection — Builds ADK agent anomaly detection with Gemini baselines, Vector Search scoring, and tiered audits.
• Combining Semantic & Keyword Search: A Hybrid Search Tutorial with Agent Platform Vector Search — Builds sparse and hybrid Vector Search indexes for Google Merch Shop product search.
• Getting Started with Text Embeddings + Agent Platform Vector Search — Builds text embeddings from Stack Overflow titles and serves semantic search with Vector Search.
• Get started with embeddings tuning on Agent Platform — Tunes text-embedding-005 for retrieval using synthetic Gemini queries and Document AI PDF chunks.
• Introduction to Gemini Multimodal Embeddings — Generates Gemini multimodal embeddings and applies them to search, similarity, PDFs, and RAG.
• Handling large-scale embedding generation for Agent Platform Vector Search — Generates large-scale text and image embeddings for Agent Platform Vector Search with throttling and checkpointing.
• Anomaly Detection of Infrastructure Logs using Gemini and BigQuery Vector Search — Detects HDFS log anomalies with Gemini summaries, text embeddings, and BigQuery Vector Search.
• Vector Search 2.0 Public Preview Quickstart — Creates a Vector Search 2.0 movie collection with CRUD, filters, semantic search, and ANN indexes.
• Agent Platform Vector Search Quickstart — Builds and queries a streaming-update Vector Search index from product embeddings.

Gemini (113)

• Evaluate a CrewAI agent on Vertex AI Agent Engine (Customized template) — Evaluates a CrewAI Gemini agent on Vertex AI Agent Engine with tool, trajectory, and response metrics.
• Evaluating a LangChain Agent on Vertex AI Agent Engine (Prebuilt template) — Deploys and evaluates a LangChain Gemini agent on Vertex AI Agent Engine using Gen AI Evaluation.
• Evaluate a LangGraph agent on Vertex AI Agent Engine (Customized template) — Builds a Gemini LangGraph agent on Agent Engine and evaluates tools, trajectories, and responses.
• Intro to Building and Deploying an Agent with Agent Engine in Vertex AI — Builds, tests, deploys, streams, customizes, and deletes a LangChain Gemini agent on Vertex AI Agent Engine.
• Building and Deploying a Human-in-the-Loop LangGraph Application with Agent Engine on Vertex AI — Builds, tests, deploys, and resumes a human-in-the-loop LangGraph agent on Vertex AI Agent Engine.
• Debugging and Optimizing Agents: A Guide to Tracing in Agent Engine — Builds, deploys, and traces a Gemini LangChain agent on Vertex AI Agent Engine.
• AG2 (formerly Autogen) Multi-Agents Example on Vertex AI Agent Engine — Builds and deploys an AG2 multi-agent research app to Vertex AI Agent Engine.
• Building and Deploying a Google Maps API Agent with Agent Engine — Builds, tests, deploys, and queries a Gemini Google Maps agent on Vertex AI Agent Engine.
• Building and Deploying a LangGraph Agent with Agent Engine in Vertex AI — Builds, tests, deploys, and deletes a Gemini LangGraph agent on Vertex AI Agent Engine.
• Building a Multi-Agent RAG Application with LangGraph and Agent Engine — Builds and deploys a LangGraph multi-agent RAG app on Vertex AI Agent Engine with Cloud SQL vector stores.
• Deploying an Agent with Agent Engine and MCP Toolbox for Databases — Deploys a Gemini hotel-booking agent on Agent Engine using Cloud SQL, Cloud Run Toolbox, and LangGraph.
• Building a Conversational Search Agent with Agent Engine and RAG on Vertex AI Search — Builds and deploys a Gemini movie-search RAG agent with LangChain, Agent Engine, and Vertex AI Search.
• Intro to Batch Inference with the Gemini API — Runs Gemini batch inference jobs using Cloud Storage and BigQuery inputs and outputs.
• Monitor batch prediction with Gemini API — Orchestrates and monitors Gemini batch predictions with Vertex AI Pipelines and BigQuery output.
• Using OpenAI libraries with Gemini on Vertex AI — Calls Gemini on Vertex AI through OpenAI Chat Completions with streaming, tools, schemas, caching, and safety.
• Intro to Gemini Agentic Vision — Uses Gemini code execution for agentic vision tasks: zooming, plotting, and image annotation.
• Intro to Context Caching with the Gemini API — Shows implicit and explicit context caching for Gemini in Vertex AI with the Google Gen AI SDK.
• Intro to Structured Output with the Gemini API — Shows how to make Gemini 2.5 Flash return JSON, enums, and schema-constrained multimodal outputs.
• Introduction to Gemini Deep Research Agent — Shows how to run Gemini Deep Research Agent with streaming, multimodal input/output, and grounding tools.
• Create & Deploy Agent and Run Gen AI Agent Evaluation — Creates, deploys, runs, and evaluates an ecommerce ADK agent on Vertex AI Agent Engine.
• Create a Gen AI Agent Evaluation for a Deployed Agent — Runs inference and creates a persisted Gen AI Agent Evaluation for a deployed Vertex AI agent.
• Evaluate images with Gecko — Evaluates prompt-image alignment with Gecko-style rubric generation and VQA validation in Vertex AI.
• Evaluating multimodal task — Evaluates image-grounded car damage labels with Vertex AI EvalTask and a Gemini custom autorater.
• Evaluate generated answers from Retrieval-Augmented Generation (RAG) using Rapid Evaluation and Dataflow ML with Vertex AI pipelines — Builds a Vertex AI Pipeline to batch-evaluate RAG Q&A outputs with Rapid Eval API and Dataflow ML.
• Evaluate videos with Gecko — Uses Vertex AI evaluation to run Gecko-style rubric generation and video validation.
• Evaluating prompts at scale with Gemini Batch Prediction API — Evaluates Gemini image-classification prompts at scale with Batch Prediction and BigQuery.
• Intro to Batch Evaluations with the Gemini API — Runs asynchronous batch evaluation of Gemini responses with Vertex AI and Cloud Storage.
• Rubric evaluation - Multimodal and Custom metric for text quality — Evaluates multimodal car-damage responses and text summaries with Vertex AI rubric-based metrics
• Evaluate Gemini Structured Output — Evaluates Gemini structured JSON extraction from scanned order forms with Vertex AI Gen AI Evaluation.
• Evaluate images with predefined Gecko — Evaluates text-to-image outputs with Vertex AI predefined Gecko image rubrics.
• Evaluate videos with predefined Gecko — Evaluates generated videos with Vertex AI Gecko text-to-video rubrics.
• Evaluate your ADK agent using Vertex AI Gen AI Evaluation service — Evaluates an ADK product research agent with Vertex AI Gen AI Evaluation metrics.
• Using Gen AI Evaluation SDK for Google Observability Gen AI multi-modal datasets — Evaluates Google Observability multimodal Gen AI data from GCS using Vertex AI Gen AI Evaluation.
• Evaluating Third-Party LLMs with the Vertex AI Gen AI Evaluation SDK — Evaluates third-party, MaaS, BYOM, and Gemini models with Vertex AI Gen AI Evaluation.
• Migrating Foundation Models: A Practical Guide with Gen AI Evaluation Serivce — Compares model migration candidates with Vertex AI Gen AI Evaluation and prompt optimization workflows.
• Gen AI Eval - Multi-turn Agent Eval, User Simulation, Metric Registration, Auto-Loss Analysis — Evaluates ADK travel agents with Vertex Gen AI Eval multi-turn simulation, custom metrics, and loss analysis.
• Multimodal Function Calling with the Gemini API & Python SDK — Shows Gemini multimodal function calling with images, video, audio, PDFs, and chat using Google Gen AI SDK.
• Intro to Gemini 2.5 Flash — Introduces Gemini 2.5 Flash on Vertex AI with text, thinking, multimodal, tools, and structured output.
• Gemini 2.5 Flash Image (Nano Banana 🍌) Generation — Generates and edits images with Gemini 2.5 Flash Image using the Google Gen AI SDK on Agent Platform.
• Gemini 3.1 Flash Image (Nano Banana 2 🍌) Generation — Shows Gemini 3.1 Flash Image generation, grounding, video input, and image editing with Google Gen AI SDK.
• Intro to Gemini 3.1 Flash-Lite — Quickstart for Gemini 3.1 Flash-Lite with Google Gen AI SDK on Google Cloud.
• Intro to Gemini 3.1 Pro — Quickstart for Gemini 3.1 Pro on Vertex AI with Google Gen AI SDK API features.
• Intro to Gemini 3.5 Flash — Quickstart for Gemini 3.5 Flash generation, multimodal inputs, tools, grounding, and safety with Google GenAI SDK.
• Intro to Gemini 3 Flash (Preview) — Quickstart for Gemini 3 Flash Preview on Vertex AI with Gen AI SDK features and multimodal/tool examples.
• Gemini 3 Pro Image (Nano Banana Pro 🍌) Generation — Shows how to generate and edit images with Gemini 3 Pro Image using the Google GenAI SDK.
• REST API — Uses cURL to call Gemini 3.5 Flash on Vertex AI for text, chat, tools, multimodal, and JSON output.
• Grounding with Vertex AI Search — Creates a Vertex AI Search engine and uses it to ground a Gemini response with retrieved context.
• Intro to Grounding with Gemini in Vertex AI — Shows how to ground Gemini 3.5 Flash responses with Search, Enterprise Web Search, Maps, and Vertex AI Search.
• Introduction to Long Context Window with Gemini on Vertex AI — Demonstrates Gemini long-context text, video, and audio prompts on Vertex AI with token counting.
• Intro to Agent Platform Multimodal Datasets — Builds Agent Platform multimodal datasets for Gemini tuning validation, resource estimates, tuning, and batch prediction.
• Getting Started with Gemini Live API using WebSocket — Uses Gemini Live API over WebSocket for text, audio, video, tools, and native audio features.
• Gemini Live API Quickstart — Connects to Gemini Live API for bidirectional audio streaming with Gen AI SDK and raw WebSockets.
• Interactive Loan Application Assistant (Financial Services) — Builds a Gemini 2.0 loan document assistant with RAG, large context, audio, Vertex AI Search, and Vector Search.
• Real-time Retrieval Augmented Generation (RAG) using the Multimodal Live API with Gemini 2.0 — Builds a retail RAG pipeline with Gemini Multimodal Live API for grounded text and audio answers.
• Nano Banana 🍌: Gemini 2.5 Flash Image Recipes — Demonstrates Gemini 2.5 Flash image generation and editing recipes on Vertex AI with google-genai.
• Get Started with Vertex AI Prompt Optimizer — Shows zero-shot and data-driven prompt optimization with Vertex AI Prompt Optimizer.
• Get started with Vertex Prompt Optimizer - Custom metric — Optimizes a Gemini prompt with Vertex AI Prompt Optimizer using a custom Cloud Function metric.
• Get Started with Vertex AI Prompt Optimizer - Long prompt — Runs Vertex AI Prompt Optimizer to improve a long Gemini prompt using data-driven evaluation.
• Get Started with Vertex AI Prompt Optimizer - Multimodality — Optimizes a multimodal Gemini prompt with Vertex AI Prompt Optimizer on MathVista image QA.
• Get Started with Vertex AI Prompt Optimizer - Tool usage — Optimizes a Gemini tool-calling system instruction with Vertex AI Prompt Optimizer data-driven mode.
• Intro to Building a Scalable and Modular RAG System with RAG Engine in Vertex AI — Builds a Vertex AI RAG Engine corpus, imports files, retrieves context, and grounds Gemini or Llama responses.
• Evaluating Vertex RAG Engine Generation with Vertex AI Python SDK for Gen AI Evaluation Service — Evaluates Vertex AI RAG Engine responses with a custom Gen AI Evaluation Service metric.
• Advanced RAG Techniques - Vertex RAG Engine Retrieval Quality Evaluation and Hyperparameters Tuning — Evaluates Vertex AI RAG Engine retrieval quality and tunes chunking, top-k, threshold, and embedding settings.
• Vertex AI RAG Engine with Vertex AI Feature Store — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Feature Store and queries it with Gemini.
• Vertex AI RAG Engine with Pinecone — Shows how to use Vertex AI RAG Engine with Pinecone as the vector database for Gemini retrieval.
• Vertex AI RAG Engine with Vertex AI Vector Search — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Vector Search and queries it with Gemini.
• Vertex AI RAG Engine with Vertex AI Search — Build a Vertex AI RAG Engine corpus backed by Vertex AI Search and query it with Gemini.
• Vertex AI RAG Engine with Weaviate — Creates a Vertex AI RAG Engine corpus backed by Weaviate and queries it with Gemini.
• Gen AI and LLM Security - ReAct and RAG attacks & mitigations — Demonstrates ReAct and RAG prompt-injection attacks with Gemini and simple mitigations.
• Building a photo recognition agent: Agent Engine setup — Sets up and deploys a Gemini LangChain agent on Agent Engine with Wikipedia and Vertex AI Search tools.
• Prepare High-Quality Preference Data for Gemini 2.5 — Prepares and filters Gemini preference data with Vertex AI Gen AI Evaluation SDK for DPO and SFT.
• Get Started with Gemini Preference Optimization — Tunes Gemini 2.5 Flash with human preference data using Vertex AI preference optimization.
• Supervised Fine-Tuning with integrated Gen AI Evaluation — Fine-tunes gemini-2.5-flash with automatic Gen AI Evaluation metrics at each checkpoint.
• Integrate Custom Metrics into Gemini Supervised Fine-Tuning — Adds a Python custom metric to Gemini SFT using Vertex AI REST tuning jobs and GCS outputs.
• Vertex AI SFT Gemini Migration Recipe — Migrates explicit SFT hyperparameters from gemini-1.5-flash-002 tuning jobs to gemini-2.5-flash.
• Supervised Fine Tuning with Gemini 2.5 Flash for Image Captioning — Fine-tunes Gemini 2.5 Flash on GCS image-caption pairs and evaluates ROUGE before and after tuning.
• Supervised Fine Tuning with Gemini 2.0 Flash for change detection using the Google Gen AI SDK — Fine-tunes Gemini 2.0 Flash on paired images for spot-the-difference change detection.
• Supervised Fine-tuning Gemini 2.5 Flash for Predictive Maintenance — Fine-tunes Gemini 2.5 Flash on simulated sensor data to classify equipment maintenance status.
• Supervised fine-tuning with Gemini 2.0 Flash for Q&A using the Google Gen AI SDK — Fine-tunes Gemini 2.0 Flash on SQuAD Q&A with Vertex AI supervised tuning and evaluates EM/F1 gains.
• Supervised Fine Tuning with Gemini 2.5 Flash for Article Summarization — Fine-tunes Gemini 2.5 Flash on WikiLingua article summaries and evaluates ROUGE before and after tuning.
• Supervised Fine-tuning Gemini 2.5 Flash for Visual Defect Detection — Fine-tunes Gemini 2.5 Flash on image-based manufacturing defect classification.
• AI-Assisted Data Science Workflows in BigQuery — Builds a BigQuery multimodal housing workflow with Gemini enrichment, BQML clustering, and vector search.
• Analyzing movie posters in BigQuery with Gemini — Analyzes movie poster images in BigQuery with Gemini, embeddings, and vector search.
• Semantic Analysis in BigQuery with AI Functions — Uses BigQuery AI functions with Gemini to rank, classify, filter, join, and enrich pet product data.
• Text + multimodal embedding generation and vector search in BigQuery — Builds text and image embeddings in BigQuery for semantic product search.
• Test Document AI Gemini — Compares Document AI entity extraction with Gemini-based extraction on a PDF.
• Analyze Multimodal Data in BigQuery — Shows BigQuery multimodal analysis over structured tables and GCS media using ObjectRefs and Gemini.
• Code Vulnerability Scanning & Automated Remediation using Gemini API in Vertex AI (Gemini 2.0) — Scans Python files from GCS with Gemini 2.0 Flash and exports vulnerability reports to CSV and JSON.
• Get hands-on with a customer support use case using Gemini and Gen AI SDK — Builds a Gemini customer-support flow for retail product matching, room-fit reasoning, tools, search, and Live API audio.
• Comparing LlamaIndex and LlamaParse for Dense Document Questioning Answering on Vertex AI — Compares LlamaIndex and LlamaParse RAG parsing methods for dense 10-Q document QA on Vertex AI.
• Document Processing with Gemini — Processes PDFs with Gemini on Vertex AI for extraction, classification, QA, summarization, translation, and page selection.
• Patents Document Understanding with Gemini — Uses Gemini batch prediction on Vertex AI to extract structured fields and figure boxes from patent PDFs.
• Sheet Music Analysis with Gemini — Uses Gemini on Vertex AI to extract and edit sheet music PDF metadata and identify a song from audio.
• Using Gemini in Education — Demonstrates Gemini education prompts across text, math, images, multiple images, and video.
• 🛡️ Agentic GraphRAG: Cybersecurity Threat Intelligence — Builds and deploys an ADK GraphRAG threat-intel agent using Neo4j and Vertex AI Agent Engine.
• 🌿 Eco-Nomad Swarm: 100% Real-Data Sustainable Travel Orchestration — Builds a Vertex AI ADK travel swarm using Neo4j GraphRAG and live APIs for sustainable trip briefs.
• Gemini: An Overview of Multimodal Use Cases — Shows Gemini multimodal prompts for text, PDFs, images, video, audio, code, retail, diagrams, and comparisons.
• Building Knowledge Graphs with Gemini — Extracts knowledge graphs from text and PDF documents with Gemini using deterministic prompts and TSV outputs.
• Creative Content Generation with Gemini in Vertex AI and Imagen — Generates and personalizes GShoe marketing copy with Gemini, then outpaints product images with Imagen.
• Video Captioning with Gemini — Uses Gemini on Vertex AI to generate rich multimodal captions from a video in Cloud Storage.
• Video Data Curation - Video Quality Filtering — Filters video datasets by metadata, OCR/watermarks, aesthetics, and motion scores for curation.
• Data Curation Pipeline: Splitting and Transcoding — Deduplicates video clips using video embeddings and BigQuery vector search.
• Data Curation Pipeline: Splitting and Transcoding — Builds a video curation pipeline for metadata filtering, splitting, scene detection, and transcoding.
• Multimodal Sentiment Analysis with Gemini — Compares Gemini sentiment analysis on audio versus a transcript of the same conversation.
• Productivity Coaching with Gemini and Google Calendar — Uses Gemini 2.5 Flash with Google Calendar data to provide productivity coaching and schedule recommendations.
• Multimodal retail recommendation: using Gemini to recommend items based on images and image reasoning — Builds a Gemini multimodal retail recommender that explains chair fit for a room image.
• Leverage LlamaIndex with Vertex AI Vector Search to perform question answering RAG — Builds LlamaIndex RAG on Vertex AI Vector Search, compares prompts, and adds multi-document agents.
• Building a Gen AI RAG application with Vertex AI Feature Store and BigQuery — Builds a LangChain RAG Q&A app using BigQuery Vector Search and Vertex AI Feature Store.
• Run RAG Pipelines in BigQuery with BQML and Vector Search — Builds a BigQuery RAG pipeline over a PDF using Document AI, embeddings, vector search, and Gemini.
• Building a Multimodal Chatbot for Warranty Claims using Gemini and Vector Search in Vertex AI — Builds a multimodal warranty-claims chatbot with Gemini, RAG, Vector Search, and function calling.
• Unlocking Multimodal Video Transcription with Gemini — Uses Gemini to build multimodal video transcripts with voice IDs, speaker names, and structured JSON output.
• Identifcation of Scene Transitions in Movies Using Gemini — Uses Gemini to detect movie scene transitions from a video and matching VTT subtitles.
• YouTube Video Analysis with Gemini — Analyzes public YouTube videos with Gemini for summaries, structured JSON extraction, and cross-video insights.

Open Models (15)

• Building an ADK agent using QWEN 3 on Vertex AI — Builds and deploys an ADK weather agent using Qwen3 on Vertex AI and Agent Engine.
• Accelerate LLM Inference with EAGLE Speculative Decoding on Vertex AI — Benchmarks EAGLE speculative decoding for Llama 4 Scout on Vertex AI against a baseline endpoint.
• Using open autorater for running evaluations with Vertex AI Gen AI Evaluation — Deploys Selene as an open judge on Vertex AI and uses Gen AI Evaluation to score LLM responses.
• MetaMath with Vertex AI Open Source Model Tuning — Fine-tunes a Llama 3.1 8B model on MetaMathQA using Vertex AI managed tuning.
• Hugging Face DLCs: Fine-tuning Gemma with Transformer Reinforcement Learning (TRL) on Vertex AI — Fine-tunes google/gemma-2b with TRL SFT and LoRA in a Vertex AI custom container job.
• Import, Deploy, and Serve custom open models on Vertex AI using Vertex AI Model Garden SDK. — Imports Hugging Face open-model weights to GCS, deploys them on Vertex AI, and serves predictions.
• Deploying Multiple LoRA Adapters on Vertex AI with vLLM — Deploys Gemma 2 with multiple LoRA adapters on Vertex AI using a custom vLLM container.
• Running a Gemma 2-based agentic RAG with Ollama on Vertex AI and LangGraph — Deploys a Gemma 2 Ollama container on Vertex AI and uses it in a LangGraph SQL RAG agent.
• Hugging Face DLCs: Serving PaliGemma using Pytorch Inference on Vertex AI with Custom Handler — Deploys gated PaliGemma from Hugging Face to Vertex AI using a PyTorch DLC and custom handler.
• Hugging Face DLCs: Serving PLLuM using Pytorch Inference on Vertex AI with Custom Handler — Deploys CYFRAGOVPL/PLLuM-12B-chat to Vertex AI with a Hugging Face PyTorch custom handler.
• Hugging Face DLCs: Serving Gemma 2 with multiple LoRA adapters with Text Generation Inference (TGI) on Vertex AI — Deploys Gemma 2 with SQL and code LoRA adapters on Vertex AI using a Hugging Face TGI custom handler.
• Use Any OSS Gen AI Model Against Your BigQuery Data — Deploys Llama 3.3 70B on Vertex AI and calls it from BigQuery ML for medical transcript analytics.
• Cloud Run GPU Inference: Gemma 2 RAG Q&A with Ollama and LangChain — Deploys Gemma 2 on Cloud Run GPU with Ollama and builds a LangChain RAG Q&A chain.
• Deploying Llama 3 on Google Kubernetes Engine with Cloud Functions and vLLM — Deploys Llama 3.2 on GKE with vLLM and exposes it through a Gen2 Cloud Function.
• Build and deploy a Hugging Face smolagent using DeepSeek-r1 on Vertex AI — Deploys DeepSeek R1 Distill Qwen 7B on Vertex AI and wraps it in a smolagents math verifier agent.

SDK (1)

• Getting started with Google Generative AI using the Gen AI SDK — Introduces Google Gen AI SDK on Vertex AI for Gemini prompts, tools, caching, batches, and embeddings.

Vertex AI Search (10)

• Create a Vertex AI Datastore and Search Engine — Creates a Vertex AI Search datastore, imports GCS PDFs, creates an enterprise search engine, and queries it.
• Custom Embeddings with Vertex AI Search — Builds a Vertex AI Search app using custom text-embedding-005 embeddings from Stack Overflow data.
• Clearbox for Ranking Tuning — Tunes Vertex AI Search ranking with ClearBox using BEIR FIQA signals and recall-based validation.
• Gemini Enterprise custom agent with prompt management — Builds a Gemini Enterprise ADK SQL agent using Vertex AI Prompt Management and schema file context.
• MCP Server with Gemini Enterprise — Builds an MCP HR leave tool on Cloud Run, connects it to an ADK Gemini agent, and registers it with Gemini Enterprise.
• Open Source Models (Gemma) as a agent with Gemini Enterprise — Deploys Gemma on Cloud Run, wraps it with ADK, deploys to Agent Engine, and registers it in Gemini Enterprise.
• Search tuning in Vertex AI Search — Tunes Vertex AI Search with JSONL/TSV Q&A data and tests a search app over Cloud Storage PDFs.
• Ingestion of Unstructured Documents with Metadata in Vertex AI Search — Ingests PDFs with metadata into Vertex AI Search and queries results with metadata filters.
• Event-based Triggering of Manual Recrawl for Vertex AI Search Advanced Website Datastores — Automates Vertex AI Search manual recrawl from JSON URL lists uploaded to Cloud Storage.
• Parsing and Chunking in Vertex AI Search: Featuring BYO Capabilities — Retrieves, reviews, exports, and reimports Vertex AI Search parsed and chunked documents with BYOC.

Tools (1)

• Tutorial for Running Prompt Management and Evaluation — Runs prompt management, MathVista evaluation, and Vertex AI prompt optimization in a Streamlit app.

Translation (2)

• Getting Started with Translation — Translates English text to Spanish with Cloud Translation API and a Cloud Storage-backed glossary.
• Generate training dataset for Cloud Translation API NMT (Neural Machine Translation) model training — Generates DOCX translation TSV data and starts Cloud Translation custom NMT training.

Vision (7)

• Imagen 3 Image Editing — Edits images with Imagen 3 using inpainting, background swap, outpainting, and mask-free prompts.
• Imagen 4 Image Upscale — Shows how to upscale generated, local, and Cloud Storage images with Imagen 4 using google-genai.
• Veo 3.1 Advanced Controls — Shows Veo 3.1 frame interpolation and video extension with Google Gen AI SDK on Agent Platform.
• Veo 3.1 Reference to Video — Generates Veo 3.1 videos from reference images using the Google Gen AI SDK.
• Veo 3.1 Video Generation — Generates Veo 3.1 videos from text and images with optional Gemini prompt optimization.
• Virtual Try-On: Image Generation — Uses Google Gen AI SDK to generate virtual try-on images from local and Cloud Storage clothing images.
• Virtual Try-On: Batch Generation Pipeline — Batch-generates virtual try-on images from person and apparel inputs using Google GenAI SDK.

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