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Gultekin"}},{"authorImage":{"id":"image-bd41fa8e15","alt":"Arun Agarwal","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?quality=85&preferwebp=true","width":"720","height":"653",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-07f1ebbf94","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/arun-agarwal/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Arun Agarwal"}},{"authorImage":{"id":"image-fdb652c0ad","alt":"Lucy Zhu","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a8252970-74b8-459d-8d57-89f09e3a0766/lucy.jpg?quality=85&preferwebp=true","width":"438","height":"512",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-1dc40cf4f3","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/lucy-zhu/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lucy Zhu"}}],"title":{"lines":["Snowflake per l’AI: portare l’intelligence dove si lavora"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12","image_647778181":"aem-GridColumn aem-GridColumn--default--12","blog_text_1389966753":"aem-GridColumn 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aem-GridColumn--default--12","quote_item":"aem-GridColumn aem-GridColumn--default--12","image_533060601":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-dce64743c2","text":"\u003Cp\u003EL’AI sta diventando il nuovo modello operativo per l’impresa. Le organizzazioni vogliono trasformare il modo in cui si lavora: dalla pianificazione delle vendite e dalle revisioni finanziarie alle operazioni con i clienti, fino alla previsione e al processo decisionale su larga scala.\u003C/p\u003E\n\u003Cp\u003EMa portare l’AI in produzione è molto più complesso di una demo. I sistemi di enterprise AI devono comprendere il contesto aziendale, scegliere gli strumenti giusti, ragionare su dati strutturati e non strutturati, rispettare le policy aziendali e migliorare continuamente, operando in modo efficiente su larga scala. Su scala enterprise, il problema più difficile non è più solo l’intelligenza del modello: è il contesto. La prossima generazione di enterprise AI sarà definita da sistemi capaci di combinare dati aziendali governati, conoscenza operativa e orchestrazione intelligente direttamente nei workflow aziendali.\u003C/p\u003E\n\u003Cp\u003ECon la piattaforma Snowflake completamente integrata per dati e AI, oltre 13.900 clienti stanno creando app AI contestualizzate e workflow operativi su dati aziendali governati.\u003C/p\u003E\n\u003Cp\u003EOggi Snowflake presenta nuove innovazioni che aiutano le organizzazioni a portare gli agenti intelligenti dove si lavora: nei workflow, nelle operazioni aziendali e nelle app enterprise, il tutto governato dagli stessi controlli di sicurezza, compliance e operatività di cui le organizzazioni si fidano già.\u003C/p\u003E\n\u003Ch2\u003EDai sistemi di insight ai sistemi di azione\u003C/h2\u003E\n\u003Cp\u003EPer anni, le piattaforme enterprise hanno aiutato le organizzazioni a capire che cosa era successo. La prossima generazione di piattaforme AI deve aiutare le organizzazioni a decidere cosa fare dopo e, sempre più spesso, a eseguire queste decisioni nei workflow e nelle app enterprise.\u003C/p\u003E\n\u003Cp\u003EQuesto porta l’AI oltre le risposte reattive alle domande, verso un’esecuzione operativa proattiva.\u003C/p\u003E\n\u003Cp\u003EInvece di assemblare manualmente dashboard, coordinare analisi e collegare workflow tra sistemi disconnessi, i team possono ora descrivere in linguaggio naturale i risultati aziendali desiderati e lasciare che i sistemi AI coordinino l’analisi, generino output e avviino workflow in tutta l’azienda.\u003C/p\u003E\n\u003Cp\u003EInvece di funzionare come un chatbot sovrapposto alle dashboard, l’enterprise AI diventa un livello di esecuzione governato per il lavoro aziendale.\u003C/p\u003E\n\u003Ch2\u003EI knowledge worker sfruttano i vantaggi dell’AI con Snowflake CoWork\u003C/h2\u003E\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/snowflake-intelligence/\"\u003ESnowflake CoWork (in precedenza Snowflake Intelligence)\u003C/a\u003E è un agente AI di lavoro personale che aiuta i knowledge worker a ragionare sui dati aziendali, automatizzare workflow e intraprendere azioni governate negli strumenti che i loro team utilizzano già.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-5cf8182dff","alt":"fig1intelligence","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b1dc4f76-8c99-4d5e-b4bc-b38255ca1702/fig1-putting-intelligence.png?quality=85&preferwebp=true","width":"1820","height":"1146",":type":"snowflake-site/components/image"},"blog_text_1170662727":{"id":"blog-text-561687ec1c","text":"\u003Cp\u003E\u003Csub\u003EFigura 1. Snowflake CoWork: l’assistente AI personale di ogni knowledge worker.\u003C/sub\u003E\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://apps.apple.com/us/app/snowflake-intelligence/id6755540372\"\u003E\u003Cb\u003EApp iOS Snowflake CoWork (è in GA a breve)\u003C/b\u003E\u003C/a\u003E permette ai dipendenti di restare connessi all’enterprise intelligence ovunque lavorino. L’autenticazione Face ID e la cronologia delle conversazioni permettono agli utenti di agire anche in mobilità, dall’aeroporto, da una riunione con un cliente o dallo show floor di una conferenza.\u003C/li\u003E\n\u003Cli\u003ELa \u003Cb\u003ESlack app (presto in private preview)\u003C/b\u003E Snowflake CoWork porta l’enterprise intelligence governata nelle conversazioni Slack: i dipendenti possono porre domande a CoWork in qualsiasi canale o thread Slack, ricevere risposte con grafici renderizzati inline e continuare la conversazione senza lasciare la piattaforma.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EAlla base di queste esperienze c’è Cortex Agents, il framework gestito Snowflake per agenti AI che permette di creare agenti di enterprise AI in grado di pianificare task e richiamare strumenti. Così i team possono fornire insight e agire nei sistemi enterprise senza gestire direttamente orchestrazione, infrastruttura o complessità di runtime.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-b3d77a4e97","alignment":"left","layout":"card","quoteSourceName":"Ramji Jagannathan","quoteSourceTitle":"VP of Enterprise Digital Platforms, Synopsys","showQuoteIcon":false,"quote":"“Con Snowflake CoWork e Cortex Agents, i responsabili aziendali non devono più affidarsi a portali isolati o passare da strumenti scollegati tra loro per ottenere risposte. Possono porre domande in linguaggio naturale direttamente dal browser o da Microsoft Teams e ricevere insight affidabili basati sui dati reali dell’azienda. Quella che era nata come un’iniziativa di trasformazione digitale si è evoluta in un modo completamente nuovo di lavorare per Synopsys, e continuiamo a individuare nuovi casi d’uso in tutta l’organizzazione.”","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1389966753":{"id":"blog-text-1e6202d67c","text":"\u003Ch3\u003ECoWork si adatta al modo di lavorare dei dipendenti grazie al contesto enterprise\u003C/h3\u003E\n\u003Cp\u003E\u003Cb\u003ECortex Sense (presto in private preview)\u003C/b\u003E apprende come un’organizzazione definisce il proprio business, inclusi workflow e relazioni tra asset di dati. Utilizzando la cronologia delle query, le dashboard e le traiettorie degli agenti AI, Cortex Sense aiuta Snowflake CoCo e Snowflake CoWork ad abilitare un ragionamento aziendale fondato fin dal primo giorno. Inoltre, la capacità di \u003Cb\u003ESemantic View Autopilot\u003C/b\u003E di importare automaticamente metriche e logica di business dalle \u003Cb\u003Edashboard Power BI (public preview)\u003C/b\u003E converte subito gli asset di BI di un’organizzazione in un livello semantico governato per gli agenti. Queste basi permettono ai dipendenti di passare dalla ricerca delle informazioni alla loro comprensione e applicazione.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EDeep Research (è in GA a breve)\u003C/b\u003E aiuta gli utenti a indagare domande di business complesse e offre prestazioni all’avanguardia sui dati aziendali, superando i \u003Ca href=\"http://www.snowflake.com/en/blog/engineering/arcticswarm-hybrid-deep-research\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Esistemi a singolo agente AI\u003C/a\u003E di oltre un terzo nel \u003Ca href=\"http://www.snowflake.com/en/blog/engineering/arcticswarm-hybrid-deep-research\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHybrid Deep Research Benchmark\u003C/a\u003E Snowflake. \u003Cb\u003EAnalytical Search (presto in public preview)\u003C/b\u003E abilita analisi calcolate su raccolte di documenti, come contratti e supporto. Insieme, queste funzionalità permettono all’enterprise AI di ragionare sull’intero contesto operativo dell’azienda.\u003C/p\u003E\n\u003Cp\u003EL’\u003Cb\u003Eorchestrazione multi-agente (presto in public preview)\u003C/b\u003E instrada automaticamente le richieste agli strumenti e ai flussi di lavoro corretti, mentre la \u003Cb\u003Ememoria\u003C/b\u003E persistente consente a CoWork di apprendere le preferenze dell’utente e le attività ricorrenti nel tempo.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EUser Skills (presto in public preview)\u003C/b\u003E permette ai team di acquisire workflow in linguaggio naturale e trasformarli in capability organizzative riutilizzabili. Le skill possono anche richiamare il \u003Cb\u003ECode Execution Tool (presto in public preview)\u003C/b\u003E per generare direttamente in CoWork output aziendali governati, come report (PDF), presentazioni incluse slide PowerPoint, analisi e visualizzazioni. Skill e plugin possono ora anche essere \u003Cb\u003Econdivisi\u003C/b\u003E (\u003Cb\u003Eprivate preview\u003C/b\u003E) tramite link, rilevati automaticamente quando poni una domanda e governati con controllo degli accessi basato sui ruoli (RBAC) integrato e scansione di sicurezza. Nel tempo, le organizzazioni sviluppano conoscenza operativa condivisa invece di workflow isolati, intrappolati in chat o note individuali.\u003C/p\u003E\n\u003Cp\u003ESnowflake CoWork rende inoltre l’enterprise intelligence riutilizzabile tra i team. \u003Cb\u003EArtifacts (è in GA a breve)\u003C/b\u003E permette agli utenti di salvare e condividere analisi live, dashboard e conversazioni mantenendo l’intero contesto e applicando controlli degli accessi basati sui ruoli. Le \u003Cb\u003Edashboard interattive (presto in public preview)\u003C/b\u003E estendono questa possibilità, permettendo ai team di esplorare le metriche in modo conversazionale e collaborare su una fonte di verità condivisa.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_647778181":{"id":"image-f0b2d9a42c","alt":"fig2intelligence","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3e7a9077-eb7d-4c2e-adfc-6f518b721150/fig2-putting-intelligence.png?quality=85&preferwebp=true","width":"1862","height":"1316",":type":"snowflake-site/components/image"},"blog_text_294827081":{"id":"blog-text-7bb17f785f","text":"\u003Cp\u003E\u003Csub\u003EFigura 2. Dashboard interattive per l’analisi collaborativa in Snowflake CoWork.\u003C/sub\u003E\u003C/p\u003E\n\u003Ch3\u003ECoWork aiuta le organizzazioni a rendere operativa l’enterprise AI in modo sicuro e su larga scala.\u003C/h3\u003E\n\u003Cp\u003E\u003Cb\u003EAutomazioni e sottoscrizioni basate sul tempo (presto in public preview)\u003C/b\u003E analizzano l’attività aziendale e inviano briefing proattivi tramite Slack, email o notifiche mobile. \u003Cb\u003EAsync Agent API (è in GA a breve)\u003C/b\u003E permette agli agenti AI di lavorare in modo asincrono su workflow e indagini di lunga durata.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAgent Studio (è in GA a breve)\u003C/b\u003E offre un ambiente centralizzato per distribuire e governare agenti enterprise. I controlli degli accessi basati sui ruoli e gli audit trail aiutano le organizzazioni a garantire che ogni interazione resti sicura e completamente verificabile. L’identità degli agenti AI fornisce un segnale riconoscibile che distingue le azioni eseguite dagli agenti AI per conto degli utenti, abilitando l’accountability operativa nei workflow enterprise.\u003C/p\u003E\n\u003Cp\u003EI \u003Cb\u003Econnettori MCP\u003C/b\u003E permettono a CoWork di operare negli strumenti enterprise che i team utilizzano già, tra cui Slack, Jira, Gmail, Salesforce e altri, sempre entro i confini di governance Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_534131104":{"id":"blog-text-bd9ade5892","text":"\u003Ch2\u003EPost-addestrare in modo efficiente modelli foundation open weight\u003C/h2\u003E\n\u003Cp\u003EI modelli sono la base di ogni workflow agentico e Snowflake investe per semplificare l’addestramento di qualsiasi modello in base ai requisiti del tuo dominio.\u003C/p\u003E\n\u003Cp\u003EQuando cercano di personalizzare workflow agentici, startup e aziende AI-native spesso si scontrano con i limiti delle opzioni dei frontier model e con la complessità di procurarsi e gestire GPU. \u003Cb\u003ECortex Training (in private preview)\u003C/b\u003E permette a startup e aziende di personalizzare modelli foundation open weight, come le famiglie di modelli Qwen o Mistral, in base ai propri requisiti di dominio, dati e costo, senza contendersi capacità GPU scarsa né preoccuparsi degli elevati costi di proprietà delle GPU dovuti a un utilizzo ridotto. Con Cortex Training, i clienti non devono gestire una complessa infrastruttura di training distribuita. Snowflake offre accesso immediato a un gruppo di GPU completamente gestito, disponibile con un utilizzo vicino al 100 % sulla base di esperimenti e demo di Snowflake Research, senza processi di prenotazione né configurazioni di orchestrazione personalizzate. Questo crea un’opportunità significativa per utenti di startup e utenti enterprise: migliorare in modo conveniente accuratezza e latenza delle soluzioni AI su larga scala, mantenendo una governance unificata in sperimentazione, valutazione e deployment.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_533060601":{"id":"image-c3adc8c99d","alt":"fig3intelligence","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--daccd58d-2780-4e61-8ed7-fe1b98054b84/fig3-putting-intelligence.png?quality=85&preferwebp=true","width":"1244","height":"912",":type":"snowflake-site/components/image"},"blog_text_1408115533":{"id":"blog-text-a9300eb04a","text":"\u003Cp\u003E\u003Csub\u003EFigura 3. Con Cortex Training, aumenta l’utilizzo fino a circa 2x per ottimizzare la spesa GPU. I risultati sopra riportati si basano su esperimenti e demo di Snowflake Research.\u003C/sub\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EResolve AI, l’AI per eseguire e gestire software in produzione, si è impegnata a investire diversi milioni di dollari in due anni per utilizzare Cortex Training e sviluppare modelli specifici per dominio tramite reinforcement learning (RL) su dati di training proprietari. Resolve AI utilizza Cortex Training per consolidare il training dei modelli in un ambiente sicuro e governato, accelerando attività che prima richiedevano di collegare infrastrutture distribuite su più piattaforme.\u003C/p\u003E\n\u003Cp\u003EResolve AI offre agenti AI specifici per dominio per la produzione, basati su harness multi-agente, ambienti simulati e workflow di valutazione. Man mano che i modelli AI vengono ulteriormente perfezionati con RL, i team hanno bisogno di un’infrastruttura in grado di gestire le componenti di training e inferenza più intensive per la GPU, senza costringerli a cedere il controllo di dati o ambienti. Resolve AI offre questo equilibrio con un’astrazione della capacità di calcolo scalabile per le prestazioni, permettendo ai clienti di mantenere il controllo di sistemi, dati e ambienti di deployment. Cortex Training offre a Resolve AI un’infrastruttura governata per eseguire in modo continuo workload di training RL, sviluppando e migliorando modelli che superano le alternative general purpose nei task di ragionamento più rilevanti per i team di engineering enterprise in produzione. Anche Snowflake è cliente di Resolve AI: i team di engineering Snowflake integrano Resolve AI nei workflow agentici esistenti per eseguire e gestire sistemi di produzione su larga scala. Questo, a sua volta, offre alle aziende esperienze AI più rapide e affidabili.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_58793132":{"id":"quote-item-cb1806d3da","alignment":"left","layout":"card","quoteSourceName":"Spiros Xanthos  ","quoteSourceTitle":"Founder e CEO, Resolve AI","showQuoteIcon":false,"quote":"“Le API model per finalità generiche continueranno a migliorare, ma esiste un limite a ciò che è possibile ottenere costruendo soluzioni basate esclusivamente su di esse. Le attività operative necessarie per eseguire e gestire sistemi software complessi presentano modalità di errore specifiche, schemi di ragionamento peculiari e requisiti di accuratezza estremamente elevati. Per superare questi limiti, utilizziamo un’infrastruttura di training progettata ad hoc, ambienti simulati su larga scala e valutazioni basate su workflow operativi reali. Cortex Training rappresenta un elemento fondamentale della nostra strategia per ampliare queste direttrici di ricerca in Resolve AI.”\r\n\r\n","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1155806208":{"id":"blog-text-4afb1cd415","text":"\u003Ch2\u003ESviluppare ed eseguire workflow di produzione più velocemente con il ML agentico\u003C/h2\u003E\n\u003Cp\u003EIl machine learning sta attraversando un cambiamento fondamentale: gli agenti di coding superano la soglia del semplice completamento automatico e arrivano a ragionamento avanzato ed esecuzione autonoma. In Snowflake, stiamo aprendo la strada alla nuova era del \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/it/blog/agentic-ml-snowflake-predictive-insights/\"\u003EML agentico\u003C/a\u003E investendo con decisione in \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/developers/guides/agentic-machine-learning-best-practices-cortex-code/\"\u003ESnowflake CoCo\u003C/a\u003E, l’agente AI di coding di Snowflake AI al centro di \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E. Grazie a skill ottimizzate per il ML e consapevolezza del contesto integrata nativamente per tutti i tuoi dati e modelli, i team ML e data science possono utilizzare Snowflake CoCo per accelerare il percorso dall’idea ai risultati business-critical \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/developers/guides/agentic-machine-learning-best-practices-cortex-code/\"\u003Edi 10 volte\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003ECon oltre 6000 account attivi mensili che eseguono milioni di modelli e migliaia di miliardi di richieste di inferenza, Snowflake ML continua a investire in miglioramenti della produttività accessibili da Cortex Code, per aiutare i team ad arrivare più rapidamente in produzione, ampliare gli strumenti di sviluppo e ottenere un’inferenza in tempo reale più veloce.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1273927023":{"id":"image-22ca5a8023","alt":"fig4intelligence","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0ebab1ca-841c-4a40-b294-0d20601e5870/fig4-putting-intelligence.png?quality=85&preferwebp=true","width":"1142","height":"435",":type":"snowflake-site/components/image"},"blog_text_1068097311":{"id":"blog-text-762028b723","text":"\u003Cp\u003E\u003Csub\u003EFigura 4. I clienti possono accelerare i workflow ML con consapevolezza nativa del contesto su dati, modelli, notebook e feature, grazie ai workflow agentici in Snowflake.\u003C/sub\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EPer lo sviluppo scalabile, puoi già addestrare modelli su terabyte di dati in \u003Ca href=\"https://www.snowflake.com/en/product/features/notebooks/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake Notebooks\u003C/a\u003E. Ora puoi sviluppare modelli da remoto in modo sicuro e fluido da VS Code e Cursor, senza spostare i dati. L’estensione Snowflake per VS Code e Cursor (presto in public preview) permette ora di connettersi ad ambienti di compute remoti, così puoi sviluppare pipeline ML su pool di compute CPU o GPU senza uscire dal tuo IDE. Inoltre, stiamo ampliando la flessibilità per utilizzare pacchetti e configurazioni propri nel tuo ambiente Snowflake. \u003Cb\u003ECustom Container Runtime (in public preview)\u003C/b\u003E permette ai team di utilizzare immagini container approvate dall’organizzazione e sottoposte a scansione di sicurezza in Snowflake Notebooks o ML Jobs.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1224809352":{"id":"image-2fdc9ea001","alt":"fig5intelligence","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1b9fa80a-31fb-4fb1-b286-64d0108fab96/fig5-putting-intelligencepng.png?quality=85&preferwebp=true","width":"786","height":"746",":type":"snowflake-site/components/image"},"blog_text_218096615":{"id":"blog-text-b01c873b6b","text":"\u003Cp\u003E\u003Csub\u003EFigura 5. HPO, xgboost, lightgbm e pytorch distribuiti e integrati nativamente offrono un rapporto prezzo-prestazioni superiore; il caso d’uso TPCx-AI mostra una \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/en/blog/engineering/snowflake-ml-runtime-vs-databricks-tpcx-ai\" target=\"_blank\"\u003Emediana di costo inferiore di 3x rispetto a Databricks per XGBoost\u003C/a\u003E con scale factor 1000. Più basso è meglio.\u003C/sub\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EPer l’inferenza a supporto delle esigenze di produzione, annunciamo feature serving più rapido, osservabilità online dei modelli e supporto multimodale per l’inferenza batch. I team ML possono ora servire feature online in 10 ms da Snowflake Feature Store con il \u003Cb\u003Esupporto delle feature in streaming (è in GA a breve).\u003C/b\u003E Questi miglioramenti offrono una freschezza dei dati <2 s dall’ingestion al serving, una migliore esperienza per gli sviluppatori grazie all’integrazione CI/CD e un calcolo efficiente delle feature aggregate su finestre temporali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_294476501":{"id":"quote-item-82ae2cd57b","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"Attentive sta modernizzando la personalizzazione del marketing su scala miliardaria, ottenendo prestazioni superiori del 68% rispetto al feature store legacy di Tecton e riducendo i costi di oltre il 30%.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_669519880":{"id":"blog-text-858926a62f","text":"\u003Cp\u003EDurante il model serving, puoi ora eseguire facilmente \u003Cb\u003EA/B testing per modelli in tempo reale (public preview) \u003C/b\u003Eper sperimentare rapidamente sul traffico live in un ambiente controllato, confrontare le prestazioni dei modelli e promuovere in produzione il modello con le prestazioni migliori. Inoltre, il \u003Cb\u003Esupporto all’inferenza per dati non strutturati (è in GA)\u003C/b\u003E abilita casi d’uso di AI come object detection, Q&A visiva e riconoscimento vocale automatico su Snowflake, senza pipeline complesse o movimentazione dei dati.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1422290882":{"id":"image-7b14a90db7","alt":"fig6intelligence","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0de3ce17-3633-4a41-a955-99738d504d06/fig6-putting-intelligence.png?quality=85&preferwebp=true","width":"525","height":"507",":type":"snowflake-site/components/image"},"blog_text_647493955":{"id":"blog-text-b4743d8260","text":"\u003Cp\u003E\u003Csub\u003EFigura 6. Esegui l’inferenza \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/scale-real-time-model-serving/\"\u003E2-5 volte più velocemente e con costi fino a 3 volte inferiori\u003C/a\u003E eliminando la movimentazione dei dati e massimizzando il throughput della capacità di calcolo. Più basso è meglio.\u003C/sub\u003E\u003C/p\u003E\n\u003Ch2\u003EIntelligenza che opera entro i confini di fiducia delle organizzazioni\u003C/h2\u003E\n\u003Cp\u003EMan mano che l’enterprise AI diventa operativa in workflow, app e sistemi decisionali, la governance diventa ancora più critica. Le innovazioni Snowflake per l’AI si basano sulla stessa data foundation affidabile su cui le organizzazioni contano per sicurezza, compliance e funzionalità di governance enterprise.\u003C/p\u003E\n\u003Cp\u003EControlli degli accessi basati sui ruoli, data masking, verificabilità, osservabilità e applicazione delle policy operano in modo coerente quando un utente aziendale interagisce con CoWork, un agente AI esegue workflow o un team distribuisce app basate sull’AI.\u003C/p\u003E\n\u003Cp\u003ESnowflake porta l’intelligenza dove si lavora, radicata nel contesto enterprise, operativa nei workflow e governata dagli stessi controlli di cui le organizzazioni si fidano già.\u003C/p\u003E\n\u003Cp\u003EPer iniziare a utilizzare l’intelligenza artificiale in Snowflake, consulta queste risorse:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ELeggi \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/en/news/press-releases/snowflake-cowork-powers-the-agentic-enterprise-as-the-personal-agent-for-knowledge-workers-to-work-smarter/\" target=\"_blank\"\u003El’annuncio ufficiale su Snowflake CoWork\u003C/a\u003E e scopri come gli agenti di lavoro governati stanno trasformando il modo in cui operano le organizzazioni.\u003C/li\u003E\n\u003Cli\u003EConsulta la \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence/getting-started\"\u003Edocumentazione\u003C/a\u003E di Snowflake CoWork, la \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/it/product/snowflake-intelligence/\"\u003Epagina del prodotto e le guide all’implementazione\u003C/a\u003E.\u003C/li\u003E\n\u003Cli\u003ESegui questa soluzione guidata per \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/\"\u003Esviluppare la tua prima pipeline di ML agentica\u003C/a\u003E.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003ETutti i riferimenti a CoWork indicano Snowflake CoWork.\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Cb\u003EDichiarazioni previsionali\u003C/b\u003E\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EQuesto articolo contiene dichiarazioni previsionali, anche in merito alle nostre future offerte di prodotto, e non costituisce un impegno a fornire alcuna offerta di prodotto. I risultati e le offerte effettivi possono differire e sono soggetti a rischi e incertezze noti e sconosciuti. Per maggiori informazioni, consulta il nostro più recente 10-Q.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_1170662727","quote_item","blog_text_1389966753","image_647778181","blog_text_294827081","blog_text_534131104","image_533060601","blog_text_1408115533","quote_item_58793132","blog_text_1155806208","image_1273927023","blog_text_1068097311","image_1224809352","blog_text_218096615","quote_item_294476501","blog_text_669519880","image_1422290882","blog_text_647493955"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"gridClassNames":"aem-Grid aem-Grid--12 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Gultekin","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3355b0f8-99db-4491-a7ae-7d4dc6d36aa8/official-baris-headshot-cropped-snowflake-wall-2.png?quality=85&preferwebp=true","width":"288","height":"288",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-167ce207fe","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/baris-gultekin/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Baris Gultekin"},"authorTitle":"Vice President of AI, Snowflake"},{"authorImage":{"id":"image-bd41fa8e15","alt":"Arun 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