What is the difference between Gemini and PaLM 2?

Gemini is the latest multimodal model (text, image, video, code). PaLM 2 is previous generation optimized for text and code. Gemini offers better reasoning and multimodal capabilities.

How to implement RAG architecture with Vertex AI?

Using Vertex AI Search or custom RAG pipeline: embeddings (textembedding-gecko), vector store (Matching Engine or AlloyDB), retrieval, augmented generation with Gemini.

What is grounding in context of Gemini?

Grounding connects LLM with external data sources - Google Search, Vertex AI Search, or custom data. Reduces hallucinations and ensures up-to-date information.

How to fine-tune Gemini model?

Vertex AI offers supervised fine-tuning with custom data. Prepare JSONL dataset, configure hyperparameters, and run tuning job. Resulting model is private.

What is Vertex AI Pipelines and why use it?

ML workflow orchestration using Kubeflow Pipelines. Automates data prep, training, evaluation, deployment. Ensures reproducibility and ML governance.

How does Dialogflow CX work for enterprise chatbots?

Visual flow builder for conversational AI. Supports multi-turn dialogs, entity extraction, webhook integrations. Native integration with Gemini for more natural responses.

What is Document AI and which documents can it process?

OCR + ML for extracting data from documents. Specialized processors for invoices, receipts, ID documents, forms, contracts. Custom processors for specific documents.

How to monitor ML models in production?

Vertex AI Model Monitoring detects training-serving skew, prediction drift, feature attribution drift. Automatic alerting on performance degradation.

What are best practices for prompt engineering?

Clear instructions, few-shot examples, chain-of-thought reasoning, structured output (JSON), temperature tuning. Vertex AI Prompt Gallery offers tested templates.

How to secure Vertex AI workloads?

VPC-SC for network isolation, CMEK for encryption, IAM for access control, audit logging, private endpoints. Vertex AI supports data residency requirements.

What is Imagen and how to use it?

Text-to-image and image editing model. Generation, editing, upscaling of images. Use via API or Vertex AI Studio. Supports negative prompts and style transfer.

Vertex AI

Vertex AI & Generative AI

Enterprise AI platforma s Gemini, PaLM 2 a kompletním MLOps ekosystémem

Gemini Models

Google's nejpokročilejší multimodal AI - text, code, images, video v jednom modelu

Conversational AI

Dialogflow CX a Agent Builder pro enterprise chatboty a virtual agents

Codey & Code Assist

AI-powered code generation, completion a review pro vývojářské týmy

Vertex AI Pipelines

MLOps orchestrace pro training, evaluation a deployment ML modelů

Imagen

Text-to-image a image editing pro generování a úpravu vizuálního obsahu

Document AI

Inteligentní extrakce dat z dokumentů, faktur, formulářů a contracts

Vertex AI — klíčová témata

Vertex AI — Gemini, Model Garden a end-to-end MLOps

Komplexní AI platforma GCP: Gemini 1.5 Pro/Flash, Model Garden, Agent Builder, Vertex AI Pipelines, Feature Store a Model Monitoring.

Gemini 1.5 Pro a Flash

2M token context window, multimodální (text/image/video/audio), function calling, JSON mode, code execution, grounding s Google Search.

Model Garden

150+ modelů: Gemini, Claude (Anthropic), Llama, Mistral, Stable Diffusion, MedLM, Codey, otevřené i proprietární modely v jednom API.

Vertex AI Agent Builder

No-code agents s grounding na vlastních datech, Dialogflow CX pro konverzační AI, Vertex AI Search (enterprise RAG), Conversation Profiles.

Vertex AI Pipelines (Kubeflow)

Kubeflow Pipelines a TFX, serverless orchestrace, lineage tracking, schedule triggers, artifact management v Cloud Storage.

Feature Store

Online + offline feature serving, point-in-time correctness, feature monitoring (drift, skew), BigQuery integrace, low-latency serving (<10ms).

Model Registry a versioning

Centrální registr verzí modelů, model evaluation metrics, lineage od training dat po deployment, RBAC s IAM.

Vertex AI Prediction (online/batch)

Auto-scaling endpoints, GPU/TPU akcelerace, traffic splitting (A/B, canary), private endpoints, batch prediction pro velké datasety.

Model Monitoring

Detekce drift (feature/prediction), schema skew, attribution drift, alerting do Cloud Monitoring, automatic retraining triggers.

AutoML (tabular/vision/text)

No-code training pro structured data, image classification/detection, text classification/extraction, transfer learning, export do edge.

Generative AI Studio a Prompt Design

Prompt engineering UI, few-shot examples, safety filters, system instructions, prompt history a versioning, evaluation framework.

RAG s Vertex AI Search

Out-of-the-box RAG pipeline, multimodal embeddings (textembedding-gecko, multimodalembedding), Vector Search, Document AI integrace.

Bezpečnost a Responsible AI

VPC Service Controls, CMEK encryption, data residency v EU, Safety filters (harm categories), Model Cards, Explainable AI (SHAP, IG).

Implementační proces

Strukturovaný přístup k AI/ML projektům

Fáze 1: Discovery

1-2 týdny

Identifikace use case
Posouzení dostupnosti dat
Výběr modelu (Gemini, PaLM, custom)
Analýza proveditelnosti a ROI
Přehled odpovědné AI
Design architektury

Fáze 2: Vývoj

3-6 týdnů

Nastavení Vertex AI Workbench
Pipeline pro přípravu dat
Trénování/fine-tuning modelů
Prompt engineering
Nastavení grounding a RAG
Evaluace a testování

Fáze 3: Nasazení

2-3 týdny

Nasazení modelu (endpoints)
API integrace
Nastavení batch prediction
Konfigurace monitoringu
A/B testing framework
Zpevnění bezpečnosti

Fáze 4: Provoz

Průběžně

Monitoring modelů a detekce drift
Continuous training pipelines
Optimalizace nákladů
Performance tuning
Verzování modelů
Integrace zpětné vazby

Technology Stack

Google AI/ML ekosystém

Foundation Models

Gemini ProGemini UltraPaLM 2CodeyImagenChirp

ML Platform

Vertex AI WorkbenchPipelinesFeature StoreModel RegistryExperiments

AI Applications

Dialogflow CXAgent BuilderDocument AITranslation AISpeech-to-Text

MLOps

Model MonitoringVertex AI PredictionTensorFlow ExtendedKubeflowCloud Build

Často kladené dotazy o Vertex AI

Odpovědi na nejčastější otázky o Google Vertex AI a generativní AI

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