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aem-GridColumn--default--12","image_1535922994":"aem-GridColumn aem-GridColumn--default--12","blog_text_1493203554":"aem-GridColumn aem-GridColumn--default--12","quote_item":"aem-GridColumn aem-GridColumn--default--12","blog_text_441075641":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-f085e4e9ef","text":"\u003Cp\u003EAl Summit Snowflake 2025 abbiamo introdotto innovazioni per rendere l’AI facile, efficiente e affidabile. Completamente gestite all’interno del perimetro sicuro di Snowflake, queste funzionalità consentono a utenti aziendali e data scientist di trasformare dati strutturati e non strutturati in insight fruibili, senza complessi strumenti o infrastrutture.\u003C/p\u003E\n\u003Cp\u003EChe si tratti di analizzare tabelle, elaborare documenti, distribuire agenti AI o addestrare modelli, queste funzionalità sono integrate nella piattaforma sicura Snowflake con governance unificata, eliminando la gestione dell’infrastruttura o strumenti complessi.\u003C/p\u003E\n\u003Cp\u003EAl Summit abbiamo presentato quattro importanti progressi nel campo dell’intelligenza artificiale:\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EData agent:\u003C/b\u003E Offriamo funzionalità intelligenti con \u003Cb\u003ESnowflake Intelligence (presto in public preview)\u003C/b\u003E e Snowflake Cortex Agents (presto in GA). Snowflake Intelligence trasforma i dati strutturati e non strutturati in azioni aziendali rapide e affidabili, mentre Cortex Agents orchestra attività in più fasi e si integra in Microsoft Teams o app personalizzate.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ECortex AISQL e analytics\u003C/b\u003E: Portando l’elaborazione dei dati multimodale tramite AI nel familiare SQL, \u003Cb\u003ECortex AISQL (in public preview)\u003C/b\u003E rende accessibili flussi di lavoro AI complessi e integra la vasta gamma di offerte per insight sui dati non strutturati di Snowflake. Questo include Document AI potenziata con estrazioni di tabelle schema-aware e un nuovo modello di estrazione “Arctic-Extract”. Questo supporta l’estrazione da documenti in 29 lingue e la possibilità di chiamata attraverso la nuova funzione AI_EXTRACT (presto in public preview).\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EModelli di AI generativa leader del settore, valutazione, osservabilità e gateway AI: L’AI Observability in Snowflake Cortex AI (presto in GA)\u003C/b\u003E consente il monitoraggio no-code e pro-code delle app Gen AI. Snowflake fornisce anche accesso ai LLM di OpenAI tramite Microsoft Azure OpenAI Service, Anthropic, Meta, Mistral e altri provider leader, sempre all’interno del perimetro sicuro di Snowflake. L’AI Governance Gateway fornisce funzionalità che consentono ai clienti di implementare facilmente criteri di governance, tra cui un controllo degli accessi efficace, un monitoraggio granulare dell’utilizzo e l’applicazione del budget (presto in private preview).\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EMachine learning (ML):\u003C/b\u003E Crea e distribuisci modelli pronti per la produzione con scalabilità e flessibilità migliorate, tra cui l’introduzione del \u003Cb\u003EData Science Agent\u003C/b\u003E autonomo per aumentare la produttività su tutti i flussi di sviluppo. \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003EInsieme, questi lanci formano una base AI unificata che semplifica lo sviluppo, scala in modo affidabile e preserva la fiducia all’interno dell’ambiente governato da Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-d615a06343","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"In Luminate, stiamo rivoluzionando il modo in cui forniamo insight basati sui dati attraverso l'intelligenza artificiale generativa. La piattaforma unificata Snowflake offre ai nostri sviluppatori un'elaborazione e un recupero scalabili per dati sia strutturati che non, costituendo la base per la creazione e l'orchestrazione di data agent che alimentano le nostre applicazioni. Cortex AI entro i confini della governance Snowflake ci consente di risparmiare tempo di sviluppo e di sfruttare appieno il potenziale dei dati del settore entertainment grazie all’Agentic AI.”\r\n—Glenn Walker, Chief Data Officer, Luminate Data\r\n","showHangingPunctuation":true,":type":"snowflake-site/components/quote-item"},"blog_text_441075641":{"id":"blog-text-52eeb37b2d","text":"\u003Ch3\u003E1. Data agent: creare agenti e analizzare dati multimodali su vasta scala\u003C/h3\u003E\r\n\u003Cp\u003EI data agent consentono di ottenere insight self-service con Snowflake Intelligence, offrendo agli utenti aziendali un’interfaccia basata sul linguaggio naturale per interrogare tutti i propri dati. Gli utenti possono porre domande complesse e ricevere risposte governate ed esplicitabili in pochi secondi senza dashboard o SQL.\u003C/p\u003E\r\n\u003Cp\u003ECortex Agents e strumenti come Cortex Analyst e Cortex Search consentono agli sviluppatori di creare applicazioni AI affidabili e pronte per la produzione che ragionano su dati strutturati e non strutturati, orchestrando i flussi di lavoro con LLM, SQL e ricerca semantica.\u003C/p\u003E\r\n\u003Cp\u003EIl Model Context Protocol (MCP) fornisce uno standard aperto per il collegamento dei sistemi AI con le fonti di dati. Siamo lieti di annunciare che il supporto per i server MCP sarà presto disponibile (presto in private preview) su Snowflake. Al lancio, gli sviluppatori potranno servire Cortex Analyst e Cortex Search come strumenti con il server MCP Snowflake. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-ea713e7ab8","height":"678","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0596e292-83ee-42e5-9db4-906546044d26/snowflake-cortex-ai.png?quality=85&preferwebp=true","alt":"Figure 1: Snowflake Cortex AI stack with the latest innovations.","lazyEnabled":true,"width":"1720","title":"Figure 1: Snowflake Cortex AI stack with the latest innovations.",":type":"snowflake-site/components/image"},"blog_text_443537801":{"id":"blog-text-13010a749c","text":"\u003Ch3\u003E1a. Sfruttare la self-service intelligence per i team aziendali\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/blog/intelligence-snowflake-summit-2025/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake Intelligence\u003C/a\u003E offre agli utenti aziendali un’interfaccia di linguaggio naturale basata sull’AI per interagire con dati sia strutturati che non strutturati. Gli utenti possono porre domande complesse in linguaggio naturale e ricevere risposte spiegabili governate in pochi secondi, senza bisogno di SQL o dashboard. Questi agenti vengono eseguiti all’interno del perimetro di sicurezza di Snowflake, applicando automaticamente controlli di accesso, mascheramento e verifica basati sui ruoli. Possono ragionare su tutti i dati aziendali, identificare le relazioni tra diverse fonti di dati e restituire risposte sintetiche da tabelle, PDF, Jira, Salesforce, Zendesk e altro ancora.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E1b. Semplifica gli insight sui dati per tutti con i data agent\u003C/p\u003E\r\n\u003Cp\u003EI data agent consentono ai team non tecnici di estrarre insight utilizzando il linguaggio naturale. Gli utenti possono vedere come vengono generati gli insight con grafici automatici, tracciabilità del lineage e explainability. Gli sviluppatori possono distribuire rapidamente nuovi casi d’uso e integrare le analisi in qualsiasi app, accelerando l’innovazione e l’impatto.\u003C/p\u003E\r\n\u003Ch3\u003E1c. Creare applicazioni conversazionali affidabili\u003C/h3\u003E\r\n\u003Cp\u003ECon Cortex Agents (presto in GA), gli sviluppatori possono creare applicazioni Gen AI che ragionano sia sui dati strutturati che su quelli non strutturati. Questi agenti producono risultati spiegabili e di alta qualità orchestrando flussi di lavoro che combinano LLM, SQL e ricerca semantica. Basati su modelli come \u003Ca href=\"https://www.snowflake.com/en/blog/anthropic-claude-3-7-sonnet-cortex/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EClaude 3.7 Sonnet\u003C/a\u003E, OpenAI GPT-4.1 e o4-mini (presto in GA), questi agenti pianificano, eseguono e perfezionano i task per ottenere risultati accurati. La explainability e l’accesso alle API integrate consentono una distribuzione e un’integrazione rapide con Microsoft Teams e Copilot, consentendo agli utenti di interagire con l’AI direttamente all’interno dei propri strumenti di collaborazione. \u003C/p\u003E\r\n\u003Ch3\u003E2. Cortex AISQL e analisi: passare dai dati multimodali agli insight con Snowflake\u003C/h3\u003E\r\n\u003Cp\u003EI dati non strutturati rimangono sottoutilizzati a causa della loro complessità. Cortex AISQL risolve questo problema consentendo ai team di analizzare documenti, immagini e altri formati utilizzando la familiare sintassi SQL, senza strumenti specializzati. Al Summit abbiamo presentato:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESQL e AI:\u003C/b\u003E Estrai metadati, classifica il sentiment o cerca all’interno di SQL.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EEstrazione di valore dai dati non strutturati:\u003C/b\u003E Utilizza Document AI, che ora supporta l’estrazione di tabelle con consapevolezza dello schema (in public preview), per estrarre tabelle strutturate da PDF complessi con una pulizia minima. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGenerazione automatica di modelli semantici (private preview)\u003C/b\u003E: Elimina la configurazione manuale dei modelli, esplora gli insight con la visualizzazione nativa dei grafici e crea branded experience utilizzando Snowpark Container Services.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003E2a. Cortex AISQL trasforma gli analisti in sviluppatori di AI\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/blog/ai-sql-query-language/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex AISQL\u003C/a\u003E ripensa SQL come linguaggio centrale per l’enterprise AI. I suoi operatori AI nativi consentono ai team di creare flussi di lavoro multimodali, combinando testo, audio, immagini e dati strutturati, senza dover imparare nuovi strumenti o duplicare i dati. \u003C/p\u003E\r\n\u003Cp\u003EMigliora le prestazioni del 30-70% rispetto a diverse pipeline tradizionali (in base ai risultati di benchmark interni, implementati per uso normale), grazie a un’inferenza batch ottimizzata e a un algoritmo di ottimizzazione delle prestazioni (in private preview), consentendo agli analisti di diventare sviluppatori di AI. \u003C/p\u003E\r\n\u003Cp\u003EI casi d’uso includono record matching, rilevamento delle frodi e join semantici di livello enterprise, tutti scritti in SQL.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E2b. Estrai valore dai dati non strutturati\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EAbbiamo inoltre presentato il nostro vision model di prossima generazione per Document AI chiamato \u003Cb\u003E“arctic extract” (private preview)\u003C/b\u003E.\u003C/p\u003E\r\n\u003Cp\u003EOra in grado di supportare un totale di 29 lingue, tra cui giapponese, coreano, tedesco, francese, spagnolo e cinese, e aggiunge funzionalità di ragionamento avanzate, incluse la classificazione e la normalizzazione.\u003C/p\u003E\r\n\u003Cp\u003ESul lato del recupero, Cortex Search aggiunge:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ERicerca fuzzy batch\u003C/b\u003E per attività ad alto throughput come la risoluzione delle entità e il rilevamento delle frodi\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAPI avanzate (in GA)\u003C/b\u003E per ricerca, valutazione e classificazione multicampo per metadati\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EInterfaccia di amministrazione in Snowsight (in GA)\u003C/b\u003E e \u003Cb\u003EQuality Evaluation Studio (presto in public preview)\u003C/b\u003E per la gestione delle ricerche no-code, la diagnostica e il tuning della pertinenza\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EI team possono anche portare \u003Cb\u003Eintegrazioni vettoriali personalizzate (public preview)\u003C/b\u003E per alimentare Cortex Search, combinando la piattaforma sicura Snowflake con output di modelli proprietari per migliorare prestazioni e controllo.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E2c. Accelerare gli insight sui dati con modelli semantici automatizzati e preview dei grafici\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ELa \u003Cb\u003Egenerazione automatica di modelli semantici (private preview)\u003C/b\u003E semplifica e velocizza la creazione dei modelli semantici per Cortex Analyst. Analizzando metadati degli schemi, cronologia delle query e dashboard, crea modelli performanti e riutilizzabili, eliminando settimane di lavoro manuale. La funzionalità Grafici (in public preview) consente agli utenti di esplorare gli insight visivamente insieme ai risultati dell’AI. \u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ESnowpark Container Services\u003C/b\u003E (in GA su AWS e Azure, presto su Google Cloud Platform) offre un runtime scalabile per ospitare app e API full-stack in modo nativo all’interno di Snowflake, con log, governance e sicurezza centralizzate.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E3. Osservabilità dell’AI generativa, scelta del modello e infrastruttura scalabile: Implementa l’AI con fiducia\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EPer aiutare le organizzazioni a scalare l’AI in modo sicuro e affidabile, Snowflake offre: \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EOsservabilità dell’AI\u003C/b\u003E: Strumenti di valutazione senza codice per accuratezza e prestazioni dell’AI generativa\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAccesso al modello\u003C/b\u003E: I principali LLM di Meta, OpenAI, Anthropic e Mistral\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EThroughput con provisioning\u003C/b\u003E: Prestazioni di inferenza prevedibili su scala di produzione\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003E3a. Valutazione e tracciamento all’interno di Cortex AI\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EL’\u003Cb\u003Eosservabilità AI (in GA) in Snowsight aiuta i team a misurare l’accuratezza e la copertura utilizzando data set di valutazione. Il punteggio di un LLM come giudice valuta fondatezza, disponibilità e nocività, eseguito in modo sicuro all’interno di Snowflake. Funzioni come i registri delle tracce degli agenti e il confronto tra modelli semplificano il debug, il perfezionamento dei prompt e la governance.\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E \u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003E3b. Accedere a OpenAI, Anthropic e altri modelli in tutta sicurezza su Snowflake\u003C/b\u003E\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cp\u003EL’ecosistema di modelli Snowflake ora include l’accesso a LLM leader di settore di OpenAI, Anthropic, Meta e Mistral, inclusi modelli di ultima generazione come GPT-4.1 e o4-mini di OpenAI tramite Microsoft Azure OpenAI Service in Azure AI Foundry e Claude Opus 4 e Claude Sonnet 4 di Anthropic. Questi modelli vengono eseguiti all’interno del perimetro di sicurezza di Snowflake, quindi i dati rimangono protetti e non vengono mai utilizzati per l’addestramento.\u003C/p\u003E\r\n\u003Cp\u003EI clienti possono abbinare il modello migliore a ogni caso d’uso, riassunto, classificazione, traduzione, ragionamento agente e altro ancora, senza dover gestire l’infrastruttura.\u003C/p\u003E\r\n\u003Cp\u003ECortex AI si sta espandendo anche a Google Cloud Platform. Con Snowpark Container Services su Google Cloud Platform (presto in GA), i clienti possono distribuire modelli open source nelle regioni GCP, evitando lo spostamento dei dati e mantenendo la governance.\u003C/p\u003E\r\n\u003Cp\u003ECortex AI si sta espandendo anche a Google Cloud Platform. \u003Cb\u003ECon Snowpark Container Services su Google Cloud Platform (presto in GA)\u003C/b\u003E, i clienti possono distribuire modelli open source nelle regioni GCP, evitando lo spostamento dei dati e mantenendo la governance.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E3c. Throughput con provisioning per l’AI enterprise-ready\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EIl throughput con provisioning (in GA su AWS e Azure) offre ai team capacità di inferenza dedicata per le app Gen AI. Accessibile tramite API REST in tutte le regioni Snowflake, consente prestazioni uniformi senza imprevedibilità dei servizi condivisi. È ideale per passare dalla POC alla produzione, senza l’overhead della configurazione dell’infrastruttura. \u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E3d. Gateway di governance dell’AI: Controllo aziendale per la Gen AI\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EL’AI Governance Gateway offre ai clienti un’unica posizione per accedere ai LLM leader del settore tramite SQL (o API REST) direttamente all’interno del perimetro sicuro di Snowflake. Grazie al controllo degli accessi basato sui ruoli (RBAC), i clienti possono implementare solidi criteri di governance. Viste granulari di monitoraggio dell’utilizzo per ogni funzionalità AI, unite ai controlli di applicazione del budget (presto in private preview), consentono ai clienti di monitorare e gestire l’utilizzo dell’AI generativa in tutta l’organizzazione. I clienti possono utilizzare Cortex Guard per filtrare i contenuti nocivi, mentre AI Observability consente di valutare, debuggare e ottimizzare le proprie app Gen AI per accuratezza e prestazioni. Questo migliora la fiducia e la trasparenza delle distribuzioni di produzione. Cortex AI porta l’AI dove si trovano i dati, e con l’AI Governance Gateway i clienti possono accelerare la distribuzione delle applicazioni Gen AI.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E4. Modelli creati e implementati in produzione con Snowflake ML\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EIl ML predittivo è ancora una pietra angolare critica per i casi d’uso come il rilevamento delle frodi, la segmentazione della clientela e i motori di raccomandazione. Tuttavia, per creare e distribuire tali modelli spesso è necessario combinare più strumenti eterogenei, difficili da governare e costosi da mantenere. \u003C/p\u003E\r\n\u003Cp\u003ECon \u003Ca href=\"https://www.snowflake.com/en/product/features/snowflake-ml/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003E, ora le aziende dispongono di una soluzione ML moderna e strettamente integrata con i dati governati in tutti i flussi di lavoro end-to-end. Clienti come \u003Ca href=\"https://reg.snowflake.com/flow/snowflake/summit25/sessions/page/catalog/session/1740376866678001Ptc8\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECoinbase\u003C/a\u003E e \u003Ca href=\"https://medium.com/snowflake/scaling-hospitality-forecasting-how-cloudbeds-leveraged-snowflake-ml-to-achieve-95-accuracy-22d0955c4956\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECloudbeds\u003C/a\u003E generano insight predittivi. Scene+, un grande programma di fidelizzazione dei clienti in Canada, ha \u003Ca href=\"https://www.snowflake.com/it/blog/scalable-model-development-production-snowflake-ml/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eabbattuto i tempi di produzione di oltre il 60% e i costi di oltre il 35% per più di 30 modelli\u003C/a\u003E utilizzando Snowflake ML.\u003C/p\u003E\r\n\u003Cp\u003EAl Summit abbiamo proseguito il nostro ritmo di innovazione con una serie di nuovi annunci incentrati sul ML scalabile e flessibile, che consentiranno ai clienti di: \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAumentare la produttività\u003C/b\u003E automatizzando la generazione di pipeline ML con Data Science Agent (presto in private preview)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ECreare modelli pronti per la produzione più velocemente\u003C/b\u003E con API di addestramento distribuite in Container Runtime (in GA) e gestire facilmente i processi di addestramento con il tracking nativo degli esperimenti (presto in private preview)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDistribuire e orchestrare facilmente pipeline ML su dati Snowflake\u003C/b\u003E, utilizzando qualsiasi IDE preferito con i processi ML (presto in GA)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EOffrire funzionalità per previsioni online a bassa latenza (presto in private preview)\u003C/b\u003E sulla capacità di calcolo scalabile del Feature Store Snowflake \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ETutto questo è integrato con l’osservabilità ML integrata per un facile monitoraggio e avvisi con supporto per metriche personalizzate. \u003C/p\u003E\r\n\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_206127400":{"id":"image-c62bd1535a","height":"1046","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fa09b394-7ad3-4cc5-a670-8ff8a8ced250/ai-ml-rollup-2.png?quality=85&preferwebp=true","alt":"Figure 2: An integrated set of development and production capabilities in Snowflake ML.","lazyEnabled":true,"width":"2590","title":"Figure 2: An integrated set of development and production capabilities in Snowflake ML.",":type":"snowflake-site/components/image"},"blog_text_866064328":{"id":"blog-text-70cb8aad2d","text":"\u003Ch3\u003E4a. Applicare l’Agentic AI al ML per accelerare la produttività.\u003C/h3\u003E\r\n\u003Cp\u003ESnowflake si impegna a rendere semplice ed efficiente l’utilizzo delle più recenti tecnologie leader del settore, inclusa la Gen AI. Vediamo un’opportunità non sfruttata di applicare le ultime innovazioni dei LLM per consentire ai data scientist. Al Summit abbiamo annunciato che applicheremo l’Agentic AI per potenziare la produttività nel ML predittivo con un Data Science Agent (presto in private preview su AWS) che itera, regola e genera automaticamente una pipeline ML completamente eseguibile a partire da semplici prompt in linguaggio naturale. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_106280478":{"id":"image-daa4d7d300","height":"644","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--692ee238-7740-47bf-a45c-7ac71f3267b5/ai-ml-rollup-3.png?quality=85&preferwebp=true","alt":"Figure 3: Data Science Agent generates ML pipelines in the form of Snowflake Notebooks cells. ","lazyEnabled":true,"width":"1081","title":"Figure 3: Data Science Agent generates ML pipelines in the form of Snowflake Notebooks cells. ",":type":"snowflake-site/components/image"},"blog_text_5183408":{"id":"blog-text-1ead2e8869","text":"\u003Cp\u003EAlimentato dai modelli di ragionamento di Anthropic, Data Science Agent utilizza la pianificazione multistep per suddividere un problema in fasi distinte e sceglie la tecnica più performante per ogni fase del flusso di lavoro ML, che include preparazione dei dati, feature engineering e addestramento. \u003C/p\u003E\r\n\u003Cp\u003EDopo aver generato rapidamente una pipeline ML verificata, i team possono fornire follow-up in base alle loro conoscenze del dominio per iterare facilmente prestazioni e precisione per la versione successiva migliore. L’output è una pipeline ML pronta per la produzione, completamente funzionale e facilmente eseguibile da \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-ml/notebooks-on-spcs\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Enotebook Snowflake in Container Runtime\u003C/a\u003E. Automatizzando il noioso lavoro di sperimentazione e debug, i data scientist possono risparmiare ore di lavoro manuale e concentrarsi su iniziative di maggiore impatto. \u003C/p\u003E\r\n\u003Ch3\u003E4b. Creare e orchestrare pipeline ML scalabili sui dati Snowflake a partire da qualsiasi IDE \u003C/h3\u003E\r\n\u003Cp\u003EPer lo sviluppo ML, abbiamo annunciato una suite di nuove funzionalità che rendono più semplice e potente la creazione di modelli su dati Snowflake utilizzando notebook Snowflake integrati in modo nativo o qualsiasi IDE esterno preferito con comodi meccanismi pushdown. I clienti possono ora accedere facilmente alle \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/container-runtime-ml\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EAPI ML distribuite in Container Runtime\u003C/a\u003E (in GA su AWS e Azure) per accelerare il caricamento dei dati, l’addestramento dei modelli e il tuning degli iperparametri da qualsiasi IDE.\u003C/p\u003E\r\n\u003Cp\u003EMan mano che le versioni dei modelli vengono iterate durante le sessioni di addestramento, il modello più performante può essere rapidamente identificato, condiviso e riprodotto dal tracking degli esperimenti integrato in modo nativo (presto in private preview), accessibile tramite API o l’interfaccia utente di Snowsight. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-201d6a9e11","youtubeVideoId":"w04A_rGLJwY","accessibilityLabel":"Accelerating Data Science: Optimizing GPU Utilization For ML Model Training In Snowflake Notebooks","layout":"fixed","youtubeWidth":"914","youtubeHeight":"514","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":true,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"blog_text_438935762":{"id":"blog-text-3ffd4366a7","text":"\u003Cp\u003EPer facilitare lo sviluppo e l’automazione delle pipeline ML, orchestrate da Snowflake Tasks o da strumenti esterni come Airflow, \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/ml-jobs/snowflake-ml-jobs\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EML Jobs\u003C/a\u003E (presto in GA su AWS e Azure) \u003C/b\u003Eoffre un comodo meccanismo per attivare l’esecuzione remota di codice ML. Inoltre, le interfacce incluse nei processi ML consentono anche ai team che preferiscono lavorare da un IDE esterno (VS Code, PyCharm, notebook SageMaker) di inviare funzioni, file o moduli al runtime container Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_528639980":{"id":"image-db6e399f32","height":"788","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--2c5fac30-258a-469f-a001-76fecfd18e2d/ai-ml-rollup-4.png?quality=85&preferwebp=true","alt":"Figure 4: Execute code remotely as an ML Job directly from any IDE (e.g., VS Code) or pipeline orchestrator.","lazyEnabled":true,"width":"792","title":"Figure 4: Execute code remotely as an ML Job directly from any IDE (e.g., VS Code) or pipeline orchestrator.",":type":"snowflake-site/components/image"},"blog_text_1493203554":{"id":"blog-text-0afb6f7aeb","text":"\u003Ch3\u003E4c. Implementare modelli ML creati ovunque su scala \u003C/h3\u003E\r\n\u003Cp\u003EIndipendentemente da dove o come viene creato, un modello può essere registrato nel Model Registry Snowflake e \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eservito per inferenza scalabile su Snowpark Container Services\u003C/a\u003E (in GA su AWS e Azure)\u003C/b\u003E, utilizzando CPU o GPU per previsioni in tempo reale o in batch. I clienti possono anche distribuire modelli a un endpoint API REST per applicazioni a bassa latenza. Questo include il supporto per la \u003Cb\u003Edistribuzione semplice di modelli da Hugging Face (presto in private preview) \u003C/b\u003Econ un clic senza scaricare alcun modello lato client. Semplicemente indicando l’handle e il task del modello per la registrazione e il serving in Snowflake, i team possono accedere istantaneamente ai migliori modelli di Hugging Face addestrati, dalla classificazione delle immagini alla somiglianza delle frasi al rilevamento degli oggetti. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1535922994":{"id":"image-9668ab90c0","height":"744","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b3c52a49-e4ce-4d77-9e2a-599e0e7b06a5/ai-ml-rollup-5.png?quality=85&preferwebp=true","alt":"Figure 5: Bring a model trained anywhere to run inference on Snowflake data.","lazyEnabled":true,"width":"1181","title":"Figure 5: Bring a model trained anywhere to run inference on Snowflake data.",":type":"snowflake-site/components/image"},"blog_text_1867648014":{"id":"blog-text-b6dd3f7edb","text":"\u003Cp\u003EAbbiamo anche annunciato che Snowflake Feature Store ora supporta anche la possibilità di fornire funzionalità per l’inferenza online a \u003Cb\u003Ebassa latenza (presto in private preview)\u003C/b\u003E oltre ai batch per supportare casi d’uso come raccomandazioni personalizzate, determinazione dei prezzi, rilevamento delle anomalie e servizio clienti. \u003C/p\u003E\n\u003Cp\u003EI modelli che memorizzano l’inferenza in Snowflake sono automaticamente integrati con le funzionalità di \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/model-observability\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EML Observability\u003C/a\u003E di Snowflake (in GA)\u003C/b\u003E, per fornire previsioni affidabili nel tempo.\u003C/p\u003E\n\u003Ch3\u003EScopri di più e visualizza le risorse\u003C/h3\u003E\n\u003Cp\u003EAl Summit abbiamo introdotto innovazioni per rendere l’enterprise AI facile da usare, efficiente da distribuire e affidabile da eseguire. Snowflake Intelligence trasforma i dati strutturati e non strutturati in insight fruibili, mentre Cortex Agents orchestra complessi task multistep su tutti i dati. \u003C/p\u003E\n\u003Cp\u003ECortex AISQL porta l’elaborazione dei dati multimodale dove si trovano flussi di lavoro SQL familiari, e l’AI Observability offre solidi strumenti di monitoraggio e valutazione per scalare le applicazioni Gen AI. Per i flussi di lavoro tradizionali, Snowflake ML rende più facile e flessibile per i clienti creare e servire modelli in produzione.\u003C/p\u003E\n\u003Cp\u003EGrazie a questi progressi, Snowflake consente alle organizzazioni di trasformare con fiducia i propri dati in azioni intelligenti su vasta scala. \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EInizia a usare Snowflake per l’AI utilizzando le seguenti risorse:\u003C/b\u003E\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EProdurre modelli ML: Prova a creare un \u003Ca href=\"https://quickstarts.snowflake.com/guide/end-to-end-ml-workflow/index.html?index=..%2F..index#1\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eflusso di lavoro ML end-to-end da Snowflake Notebooks\u003C/a\u003E (disponibile dalla \u003Ca href=\"https://signup.snowflake.com/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eprova gratuita\u003C/a\u003E) o da qualsiasi IDE preferito\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Ci\u003EAffermazioni riferite al futuro\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EQuesto articolo contiene delle affermazioni riferite al futuro, tra cui offerte future di prodotti, che però non rappresentano un impegno a fornire alcuna offerta di prodotti. Le offerte e i risultati effettivi potrebbero essere diversi ed essere soggetti a incertezze e rischi noti e non noti. Fai riferimento al nostro più recente modulo 10‑Q per ulteriori informazioni.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","quote_item","blog_text_441075641","image","blog_text_443537801","image_206127400","blog_text_866064328","image_106280478","blog_text_5183408","youtube","blog_text_438935762","image_528639980","blog_text_1493203554","image_1535922994","blog_text_1867648014"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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