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12, 2024","image":{"id":"image-e933feb019","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b06aed21-6867-4295-81c2-f1ca198d475c/build2024-ai-ml-blogheader-680x720.jpg?quality=85&preferwebp=true","alt":"Photo illustration of hands on laptop with background screens and the BUILD 2024 logo at the bottom on a blue bar","lazyEnabled":true,"height":"720","width":"1680",":type":"snowflake-site/components/image"},"authors":[{"authorImage":{"id":"image-14551fa06d","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a8252970-74b8-459d-8d57-89f09e3a0766/lucy.jpg?quality=85&preferwebp=true","alt":"Lucy Zhu","lazyEnabled":true,"height":"512","width":"438",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-3f14622514","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"}},{"authorImage":{"id":"image-9597b71e73","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?quality=85&preferwebp=true","alt":"Arun Agarwal","lazyEnabled":true,"height":"653","width":"720",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-ff38cb109d","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"}}],"tag":{"tagText":"Prodotto e tecnologia","tagColor":"#3F808A"},"title":{"lines":["Crea più rapidamente un’AI di livello enterprise con il nuovo supporto multimodale, l’osservabilità migliorata e altro ancora"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnClassNames":{"youtube":"aem-GridColumn aem-GridColumn--default--12","image":"aem-GridColumn aem-GridColumn--default--12","image_79610950":"aem-GridColumn aem-GridColumn--default--12","image_460253025":"aem-GridColumn aem-GridColumn--default--12","blog_text_10630711":"aem-GridColumn aem-GridColumn--default--12","blog_text_661965933":"aem-GridColumn aem-GridColumn--default--12","blog_text_1553733623":"aem-GridColumn aem-GridColumn--default--12","image_372176798":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12","blog_text_1486098648":"aem-GridColumn aem-GridColumn--default--12","image_2080796899":"aem-GridColumn aem-GridColumn--default--12","blog_text_1084608810":"aem-GridColumn aem-GridColumn--default--12","blog_text_531091591":"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-00d0cb96d7","text":"\u003Cp\u003EDurante \u003Ca href=\"https://www.snowflake.com/build/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBUILD\u003C/a\u003E, Snowflake ha presentato nuove potenti funzionalità progettate per accelerare la creazione e la distribuzione di applicazioni Gen AI a partire dai dati aziendali, aiutandoti al contempo a garantire fiducia e sicurezza. Questi nuovi strumenti semplificano i flussi di lavoro, forniscono insight su vasta scala e portano rapidamente in produzione le app AI. Clienti come \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/skai/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESkai\u003C/a\u003E hanno utilizzato queste funzionalità per portare in produzione la loro soluzione di AI generativa in soli due giorni anziché mesi. \u003C/p\u003E\n\u003Cp\u003EEcco come Snowflake \u003Ca href=\"https://www.snowflake.com/it/data-cloud/cortex/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex AI\u003C/a\u003E e \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowflake-ml/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003E stanno accelerando la fornitura di soluzioni AI affidabili per le applicazioni di AI generativa più critiche:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EElaborazione del linguaggio naturale (NLP) per le pipeline di dati: \u003C/b\u003EI large language model (LLM) hanno un potenziale trasformativo, ma spesso integrano in batch l’inferenza nelle pipeline, il che può essere complesso. Ora, con una semplice query, gli sviluppatori possono dedurre il sentiment o classificare le recensioni dei clienti in milioni di record, migliorando l’efficienza e risparmiando tempo. Ad esempio, \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/ts-imagine/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETS Imagine\u003C/a\u003E ha implementato l’AI generativa su vasta scala con Snowflake Cortex AI, riducendo i costi del 30% e risparmiando 4000 ore precedentemente dedicate ad attività manuali. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EApp conversazionali: \u003C/b\u003ECreare risposte affidabili e coinvolgenti per le domande degli utenti ora è più semplice, aprendo le porte a potenti casi d’uso come l’analisi self-service e la ricerca di documenti tramite \u003Ca href=\"https://www.snowflake.com/build/americas/agenda?agendaPath=session/1468431\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Echatbot.\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESviluppo e distribuzione di modelli basati su GPU: \u003C/b\u003ECrea potenti modelli ML avanzati con i tuoi pacchetti Python preferiti su GPU o CPU che li utilizzano per l’inferenza in container, sempre sulla stessa piattaforma dei tuoi dati governati. Clienti come \u003Ca href=\"https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EAvios, CHG Healthcare e Keysight Technologies\u003C/a\u003E stanno già sviluppando modelli basati su container in Snowflake ML.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003EOttimizza le prestazioni della pipeline NLP con un’inferenza batch LLM conveniente \u003C/h2\u003E\n\u003Cp\u003ECon la tecnologia in rapida evoluzione, i modelli sono disponibili in varie dimensioni, context windows e funzionalità, rendendo essenziale selezionare quello giusto per il proprio caso d’uso specifico. Ad esempio, se i documenti sono in più lingue, un LLM con solide funzionalità multilingue è fondamentale. Tuttavia, per attività di NLP più semplici, come la classificazione, un modello avanzato potrebbe essere eccessivo e un LLM più piccolo potrebbe essere più efficace. Le \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Efunzioni LLM di Cortex\u003C/a\u003E offrono modelli ottimizzati per casi d’uso specifici, come traduzione, sintesi e classificazione. Questi modelli scalabili sono in grado di gestire milioni di record, consentendo di creare in modo efficiente pipeline di dati NLP ad alte prestazioni. Tuttavia, la scalabilità dell’elaborazione dei dati LLM a milioni di record può comportare sfide di trasferimento e orchestrazione dei dati, facilmente risolvibili grazie alle facili funzioni SQL di Snowflake Cortex.\u003C/p\u003E\n\u003Ch3\u003EOttimizza prestazioni e costi con una più ampia gamma di opzioni di modelli\u003C/h3\u003E\n\u003Cp\u003ECortex AI fornisce un facile accesso ai modelli leader del settore tramite funzioni LLM o API REST, consentendoti di concentrarti sulla promozione delle innovazioni dell’AI generativa. Offriamo un’ampia scelta di modelli in varie dimensioni, lunghezze delle context window e supporti linguistici. Le recenti aggiunte includono il \u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-invests-in-voyage-ai-optimizing-multilingual-rag/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Emodello multilingue Voyage\u003C/a\u003E, i modelli \u003Ca href=\"https://www.snowflake.com/en/blog/meta-llama-enterprise-apps-snowflake-cortex-ai/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ELlama 3.1\u003C/a\u003E e \u003Ca href=\"https://medium.com/snowflake/metas-llama-3-2-50347680b3f4\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E3.2\u003C/a\u003E di Meta e il \u003Ca href=\"https://medium.com/snowflake/applying-jamba-instruct-to-long-context-use-cases-in-snowflake-cortex-ai-fcbbb9fc2df3\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Emodello Jamba-Instruct di AI21\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003ECon Cortex Playground (presto in public preview), puoi provare i modelli direttamente in Snowsight. Questa interfaccia senza codice consente di sperimentare, confrontare e valutare rapidamente i modelli non appena diventano disponibili. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-46cde075a5","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ea48f40d-d0f4-4e37-9158-c62daa775f3b/build2024-ai-ml-cortex-playground.jpg?quality=85&preferwebp=true","alt":"3 screenshots showing Snowflake Cortex Playground, the settings controls, and the View SQL capability","lazyEnabled":true,"height":"938","width":"1432","title":"Figura 1: Cortex Playground, disponibile in public preview, fornisce un’interfaccia senza codice per la generazione di LLM e il confronto delle impostazioni.",":type":"snowflake-site/components/image"},"blog_text_10630711":{"id":"blog-text-ed7de05893","text":"\u003Cp\u003ELe tecniche di personalizzazione dei modelli consentono di ottimizzarli per il caso d’uso specifico. Snowflake sta introducendo il \u003Cb\u003Efine‐tuning serverless\u003C/b\u003E (presto in GA), che consente agli sviluppatori di ottimizzare i modelli per migliorare il rapporto costi‐prestazioni. Questo servizio completamente gestito elimina la necessità per gli sviluppatori di creare o gestire la propria infrastruttura per l’addestramento e l’inferenza. \u003C/p\u003E\n\u003Ch3\u003ESupporto multimodale nella funzione COMPLETE\u003C/h3\u003E\n\u003Cp\u003EMigliora le app e le pipeline AI con il supporto multimodale per risposte più complete. Con le nuove funzionalità di AI generativa, ora gli sviluppatori possono elaborare i dati multimodali utilizzando le informazioni più pertinenti nelle loro applicazioni. Stiamo attivando l’\u003Cb\u003Einferenza LLM\u003C/b\u003E multimodale (presto in private preview) come parte della \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#available-functions\" target=\"_blank\"\u003E\u003Cb\u003Efunzione Cortex COMPLETE\u003C/b\u003E\u003C/a\u003E per gli input di immagine utilizzando i modelli Llama 3.2 disponibili in Snowflake Cortex AI. Il supporto per l’integrazione di audio, video e immagini seguirà presto. Il supporto multimodale esteso arricchisce le risposte per vari task, come la sintesi, la classificazione e l’estrazione delle entità su vari tipi di media. \u003C/p\u003E\n\u003Ch3\u003EAnalisi multimodale con sintassi SQL familiare\u003C/h3\u003E\n\u003Cp\u003ELe query sul database sono la forza sottostante che consente alle organizzazioni di generare insight e fornisce agli utenti esperienze data-driven. Tradizionalmente, SQL era limitato ai dati strutturati organizzati ordinatamente in tabelle. Snowflake introdurrà a breve nuove funzioni SQL multimodali (presto in private preview) che consentono ai team dati di eseguire flussi di lavoro analitici su dati non strutturati, come le immagini. Con queste funzioni, i team possono eseguire attività come filtri semantici e join su data set non strutturati utilizzando la familiare sintassi SQL. \u003C/p\u003E\n\u003Ch3\u003EElaborare con sicurezza grandi processi di inferenza con capacità di throughput fornita\u003C/h3\u003E\n\u003Cp\u003EUn’esperienza dell’utente finale coerente è spesso un fattore di convergenza quando gli sviluppatori vanno oltre le proof of concept. Con \u003Cb\u003EProvisional Throughput\u003C/b\u003E (presto in public preview su AWS), i clienti possono prenotare throughput dedicati, garantendo prestazioni coerenti e prevedibili per i propri workload. Inoltre, abbiamo \u003Ca href=\"https://medium.com/snowflake/announcing-cross-region-inference-on-snowflake-cortex-ai-ab3baed935ce\" target=\"_blank\"\u003Elanciato l’inferenza cross-region\u003C/a\u003E, che consente di accedere ai LLM preferiti anche se non sono disponibili nella regione principale.\u003C/p\u003E\n\u003Ch2\u003ESviluppa app AI conversazionali di alta qualità più velocemente \u003C/h2\u003E\n\u003Cp\u003EOra Snowflake offre nuovi strumenti per semplificare lo sviluppo e la distribuzione di applicazioni AI conversazionali.\u003C/p\u003E\n\u003Ch3\u003EPre-elaborazione avanzata dei documenti per RAG\u003C/h3\u003E\n\u003Cp\u003EAll’inizio di quest’anno abbiamo lanciato \u003Ca href=\"https://www.snowflake.com/en/blog/cortex-search-ai-hybrid-search/\" target=\"_blank\"\u003E\u003Cb\u003ECortex Search\u003C/b\u003E\u003C/a\u003E per aiutare i clienti a estrarre insight dai dati non strutturati e trasformare enormi raccolte di documenti in risorse pronte per l’AI senza scrivere codice complesso. La soluzione di recupero completamente gestita consente agli sviluppatori di creare app AI scalabili che estraggono insight dai dati non strutturati all’interno dell’ambiente sicuro di Snowflake. Questa funzionalità è particolarmente potente se abbinata a funzioni di estrazione e chunking conformi al layout, che ottimizzano il recupero dei documenti semplificando la preelaborazione tramite brevi funzioni SQL.\u003C/p\u003E\n\u003Cp\u003EOra puoi preparare più rapidamente i documenti AI con due nuove funzioni di pre-elaborazione SQL. Stiamo introducendo soluzioni semplificate per l’elaborazione dei documenti da blob storage (p.es., Amazon S3) alle rappresentazioni di testo per l’utilizzo in applicazioni di Retrieval-Augmented Generation (RAG). Ora gli utenti SQL possono sostituire complesse pipeline di elaborazione dei documenti con semplici funzioni SQL di Cortex AI, come\u003Cb\u003E \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/parse-document\" target=\"_blank\"\u003EPARSE_DOCUMENT\u003C/a\u003E\u003C/b\u003E (public preview) e \u003Cb\u003ESPLIT_TEXT_RECURSIVE_Character\u003C/b\u003E (private preview). La funzione di analisi si occupa di estrarre testo e layout dai documenti. Gli sviluppatori non devono spostare i dati grezzi dalla posizione di archiviazione originale. La funzione di suddivisione del testo gestisce il chunking del testo estratto in segmenti più ottimizzati per l’indicizzazione e il recupero. \u003Ca href=\"https://www.snowflake.com/en/blog/streamline-rag-document-preprocessing/\" target=\"_blank\"\u003EScopri di più.\u003C/a\u003E\u003C/p\u003E\n\u003Ch3\u003EMiglioramenti dell’analisi conversazionale in Cortex Analyst\u003C/h3\u003E\n\u003Cp\u003EAmplia la portata delle analisi self-service accurate in linguaggio naturale con Cortex Analyst. \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\" target=\"_blank\"\u003ESnowflake Cortex Analyst\u003C/a\u003E\u003C/b\u003E continua a evolversi come servizio completamente gestito, fornendo analisi conversazionali self-service che consentono agli utenti di interagire in modo trasparente con i dati strutturati in Snowflake. I recenti aggiornamenti migliorano l’esperienza d’uso e la profondità analitica, tra cui il \u003Cb\u003Esupporto SQL Joins per schemas Star e Snowflake \u003C/b\u003E (public preview) mantenendo una qualità elevata, rendendo possibili esplorazioni dei dati più complesse e insight più approfonditi. Inoltre, le \u003Cb\u003Econversazioni multiturno\u003C/b\u003E (public preview) consentono agli utenti di porre domande successive per interazioni più fluide. L’\u003Cb\u003Eintegrazione con Cortex Search\u003C/b\u003E (public preview) migliora l’accuratezza delle query SQL generate recuperando dinamicamente valori letterali esatti o simili per campi di dati complessi ad alta cardinalità, mentre il \u003Cb\u003Econtrollo degli accessi basato sui ruoli a livello API\u003C/b\u003E rafforza la sicurezza e la governance. \u003C/p\u003E\n\u003Cp\u003EInsieme, questi aggiornamenti consentono alle aziende di ottenere insight accurati e tempestivi dai loro dati, riducendo il costo complessivo del processo decisionale data-driven. Per ulteriori informazioni su queste nuove funzionalità e i relativi aggiornamenti, leggi il nostro \u003Ca href=\"https://medium.com/@sri.chintala/e77632d99739?source=friends_link&sk=edcb3728bf92f1d9cfa2947814032ba4\" target=\"_blank\"\u003Eblog Cortex Analyst\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003EStrumenti avanzati di orchestrazione e osservabilità per le app LLM\u003C/h3\u003E\n\u003Cp\u003ERiduci l’integrazione e l’orchestrazione manuali nelle applicazioni chat con \u003Cb\u003ECortex Chat API\u003C/b\u003E (presto in public preview), che semplifica la creazione di applicazioni interattive in Snowflake. Combinando recupero e generazione in una singola chiamata API, ora è possibile creare app chat agentiche per parlare con dati sia strutturati che non strutturati. Il prompt ottimizzato consente risposte di alta qualità insieme a citazioni che riducono le allucinazioni e aumentano la fiducia. Un unico endpoint di integrazione semplifica l’architettura dell’applicazione. \u003C/p\u003E\n\u003Cp\u003EAumenta l’affidabilità delle app AI con la valutazione e il monitoraggio integrati attraverso le nuove \u003Cb\u003EAI Observability for LLM Apps\u003C/b\u003E (in private preview). Questa suite di osservabilità fornisce strumenti essenziali per migliorare la valutazione e la fiducia nelle applicazioni LLM, supportando le attività di conformità dell’AI dei clienti. Queste funzioni di osservabilità consentono agli sviluppatori di app di valutare metriche di qualità, come pertinenza, fondatezza e bias, oltre a metriche di performance tradizionali, come la latenza, durante tutto il processo di sviluppo. Inoltre, consentono un monitoraggio accurato dei log delle applicazioni, consentendo alle organizzazioni di tenere d’occhio le proprie applicazioni AI. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_372176798":{"id":"image-58f02777d7","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8ea31cab-7019-41aa-a28b-96a922b28694/build2024-ai-ml-observability.png?quality=85&preferwebp=true","alt":"Three layered screenshots showing Snowflake ML Observability capability","lazyEnabled":true,"height":"1285","width":"2048","title":"Figura 2. L’osservabilità dell’AI integrata per i LLM è disponibile in private preview per lo sviluppo di app AI di alta qualità.",":type":"snowflake-site/components/image"},"blog_text_1486098648":{"id":"blog-text-091bdf8e00","text":"\u003Cp\u003EOra gli sviluppatori di AI possono monitorare e valutare in modo trasparente le metriche delle prestazioni delle app, scegliendo modelli, prompt e servizi di recupero ottimizzati per i loro casi d’uso specifici. Inoltre, gli sviluppatori possono gestire i log e sfruttare il monitoraggio preintegrato per le app all’interno di Snowflake o per le app esterne utilizzando la \u003Ca href=\"https://www.trulens.org/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Elibreria open source TruLens\u003C/a\u003E, che Snowflake supervisiona nel quadro dell’\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-acquires-truera-to-bring-llm-ml-observability-to-data-cloud/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eacquisizione di TruEra\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003ENuove integrazioni delle sorgenti dati\u003C/h3\u003E\n\u003Cp\u003EPorta l’AI vicino a più dati con nuove integrazioni delle fonti di dati. \u003Cb\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZTP0KMY/snowflake-snowflake-connector-for-sharepoint\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake Connector for SharePoint\u003C/a\u003E\u003C/b\u003E (public preview) consente ai team dati di creare applicazioni AI sopra i dati SharePoint in Snowflake, senza bisogno di configurare manualmente pipeline o pre-elaborare i dati e aderendo ai criteri di accesso esistenti. \u003C/p\u003E\n\u003Cp\u003EInoltre, ora puoi migliorare le funzionalità dei chatbot con \u003Cb\u003ECortex Knowledge Extensions\u003C/b\u003E (in private preview) nel Marketplace Snowflake. Queste estensioni consentono ai team dati di arricchire i chatbot AI aziendali con contenuti recenti e proprietari di provider terzi, come ricerche o pubblicazioni di giornali. Per gli editori e i provider di contenuti, questo apre un nuovo flusso di entrate e protegge anche la proprietà intellettuale da un utilizzo non autorizzato, ad esempio per l’addestramento dei LLM. E per i consumatori, fornisce un accesso più rapido a risposte AI di alta qualità, senza preoccupazioni per la qualità o la conformità commerciale.\u003C/p\u003E\n\u003Ch2\u003EOttieni insight ML rapidi e affidabili con le GPU\u003C/h2\u003E\n\u003Cp\u003EPoiché le organizzazioni accumulano più dati in un’ampia varietà di formati e le tecniche di modellazione diventano sempre più sofisticate, i compiti di un data scientist e di un ML engineer diventano sempre più complessi. \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowflake-ml/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003E fornisce i componenti fondamentali di cui i team di data science e ML hanno bisogno per passare rapidamente dal prototipo alla produzione di funzioni e modelli sulla stessa piattaforma utilizzata per governare e gestire i propri dati. Organizzazioni come \u003Ca href=\"https://www.snowflake.com/webinars/customer-webinars/show-me-your-architecture-technical-deep-dive-of-chg-healthcares-ml-and-gen-ai-strategy-for-stronger-customer-relationship-management-2024-07-31/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECHG Healthcare\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/webinars/thought-leadership/building-a-personalized-chatbot-app-with-rag-and-snowflake-feature-store-2024-09-17/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStride\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/igs-energy/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EIGS Energy\u003C/a\u003E e \u003Ca href=\"https://medium.com/snowflake/from-zero-to-value-unlocking-rapid-insights-with-an-end-to-end-ml-workflow-in-snowflake-9b000a25723c\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECooke Aquaculture\u003C/a\u003E stanno creando sofisticati modelli ML end-to-end direttamente in Snowflake. Di recente abbiamo annunciato nuove innovazioni per lo sviluppo e il serving di modelli ML con GPU distribuite per casi d’uso avanzati come \u003Ca href=\"https://quickstarts.snowflake.com/guide/getting-started-with-running-distributed-pytorch-models-on-snowflake/?_ga=2.109862725.1770139711.1729530235-871656794.1699564848&_gac=1.86430442.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Esistemi di raccomandazione\u003C/a\u003E, \u003Ca href=\"https://quickstarts.snowflake.com/guide/defect_detection_using_distributed_pyTorch_with_snowflake_notebooks/index.html?index=..%2F..index&_ga=2.109862725.1770139711.1729530235-871656794.1699564848&_gac=1.86430442.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Ecomputer vision\u003C/a\u003E, \u003Ca href=\"https://quickstarts.snowflake.com/guide/scale-embeddings-with-snowflake-notebooks-on-container-runtime/index.html?index=..%2F..index&_ga=2.109862725.1770139711.1729530235-871656794.1699564848&_gac=1.86430442.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Ecustom embedding\u003C/a\u003E e \u003Ca href=\"https://quickstarts.snowflake.com/guide/train-an-xgboost-model-with-gpus-using-snowflake-notebooks/?_ga=2.114671431.1770139711.1729530235-871656794.1699564848&_gac=1.27590350.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Emodelli decision tree\u003C/a\u003E. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2080796899":{"id":"image-5e63653bfe","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c14bd3fb-79e3-4fe1-b8e1-3fda0bdb5ff4/build2024-ai-ml-snowflake-ml.png?quality=85&preferwebp=true","alt":"Diagram of Snowflake ML Capabilities","lazyEnabled":true,"height":"1087","width":"2564","title":"Figura 3. Snowflake ML semplifica lo sviluppo di modelli e gli MLOps su un’unica piattaforma. ",":type":"snowflake-site/components/image"},"blog_text_1084608810":{"id":"blog-text-0b12cd0142","text":"\u003Ch3\u003EAccelera lo sviluppo ML con notebook basati su GPU\u003C/h3\u003E\n\u003Cp\u003ELe GPU offrono una potente capacità di calcolo che velocizza le attività ML ad alta intensità di risorse, come l’addestramento dei modelli. Questa accelerazione dell’elaborazione migliora significativamente la velocità di iterazione e distribuzione dei modelli, soprattutto quando si lavora con data set di grandi dimensioni o si utilizzano framework di deep learning avanzati come PyTorch. Per supportare flussi di lavoro ad alta intensità di risorse senza dover spostare grandi volumi di dati e senza limitazioni al codice o alle librerie che possono utilizzare, Snowflake ML ora supporta \u003Ca href=\"https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/\" target=\"_blank\"\u003E\u003Cb\u003EContainer Runtime\u003C/b\u003E\u003C/a\u003E (public preview su AWS e presto public preview su Azure) accessibile tramite \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\" target=\"_blank\"\u003E\u003Cb\u003ESnowflake Notebooks\u003C/b\u003E\u003C/a\u003E (in GA), una superficie di sviluppo interattiva basata su celle unificata che combina Python, SQL e Markdown. \u003C/p\u003E\n\u003Cp\u003EA seguito di test interni, abbiamo scoperto che le API ML Snowflake in Container Runtime possono eseguire in modo efficiente processi di addestramento ML sulle GPU con una \u003Ca href=\"https://www.snowflake.com/engineering-blog/machine-learning-container-runtime/\" target=\"_blank\"\u003Evelocità di esecuzione\u003C/a\u003E 3-7 volte superiore rispetto all’esecuzione dello stesso workload con i dati Snowflake utilizzando librerie open source al di fuori del runtime. Questo runtime basato su container completamente gestito è dotato delle librerie e dei framework Python più diffusi, con la flessibilità necessaria per l’estensione da hub open source come PyPI e Hugging Face.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-576c5d8c36","youtubeVideoId":"MD5aP0uEAps?si=YBYnF2S0RSseWpC7","layout":"fixed","youtubeWidth":"840","youtubeHeight":"422","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_531091591":{"id":"blog-text-ddfacc3fd2","text":"\u003Ch3\u003EScala orizzontalmente l’inferenza nei container con le GPU\u003C/h3\u003E\n\u003Cp\u003EDopo lo sviluppo, puoi servire modelli per la produzione da Snowflake Model Registry per qualsiasi modello ML, LLM o embedding utilizzando CPU o GPU distribuite in \u003Ca href=\"https://www.snowflake.com/en/blog/secure-app-deployment-snowpark-container-services-ga/\" target=\"_blank\"\u003E\u003Cb\u003ESnowpark Container Services\u003C/b\u003E\u003C/a\u003E (in GA in Azure e AWS). \u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/\" target=\"_blank\"\u003EModel Serving in Container\u003C/a\u003E\u003C/b\u003E (public preview in AWS) consente un’inferenza più veloce e potente utilizzando istanze GPU on demand senza bisogno di ottimizzare manualmente per l’utilizzo delle risorse.\u003C/p\u003E\n\u003Ch3\u003ERileva rapidamente il degrado del modello con il monitoraggio integrato\u003C/h3\u003E\n\u003Cp\u003EPer mantenere l’inferenza dei modelli ML in produzione, puoi utilizzare le funzionalità MLOps (operazioni di machine learning integrate in modo nativo) di Snowflake, tra cui \u003Ca href=\"https://docs.snowflake.com/en/alias/snowflake-ml/ml-observability\" target=\"_blank\"\u003E\u003Cb\u003EObservability for ML Models\u003C/b\u003E\u003C/a\u003E (public preview). Ora i team possono monitorare, impostare avvisi e risolvere rapidamente degrado, deriva e altre metriche del modello direttamente dalle dashboard integrate collegate al Model Registry Snowflake. La piattaforma include anche \u003Ca href=\"https://medium.com/snowflake/snowflake-ml-explainability-now-in-public-preview-f9e65f9d4d44\" target=\"_blank\"\u003E\u003Cb\u003EML Explainability\u003C/b\u003E\u003C/a\u003E (public preview), che consente agli utenti di calcolare facilmente i valori Shapley per i modelli registrati nel Model Registry Snowflake, addestrati internamente o esternamente. \u003C/p\u003E\n\u003Cp\u003EQueste nuove funzionalità di monitoraggio ML si aggiungono alle \u003Ca href=\"https://www.snowflake.com/webinars/thought-leadership/unified-and-governed-mlops-in-snowflake-2024-04-30/\" target=\"_blank\"\u003Efunzionalità MLOps\u003C/a\u003E disponibili in Snowflake ML, tra cui \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\" target=\"_blank\"\u003E\u003Cb\u003EModel Registry\u003C/b\u003E\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/ml-lineage\" target=\"_blank\"\u003E\u003Cb\u003EML Lineage\u003C/b\u003E\u003C/a\u003E (public preview) e \u003Ca href=\"https://www.snowflake.com/en/blog/build-manage-production-ml-snowflake-feature-store/\" target=\"_blank\"\u003E\u003Cb\u003EFeature Store\u003C/b\u003E\u003C/a\u003E (general availability).  \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_79610950":{"id":"image-2024165b1a","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--cb3fb17d-4610-44da-a97a-1f6a1cff3c6a/build2024-ai-ml-model-registry.png?quality=85&preferwebp=true","alt":"Screenshot of Snowflake Model Registry monitoring","lazyEnabled":true,"height":"919","width":"1276","title":"Figura 4. Gli utenti possono monitorare le prestazioni dei modelli ML in produzione direttamente dall’interfaccia utente Snowsight integrata con Snowflake Model Registry.",":type":"snowflake-site/components/image"},"blog_text_661965933":{"id":"blog-text-d2af098afe","text":"\u003Ch2\u003ESfrutta tutta la potenza dei dati aziendali con gli agenti AI in Snowflake Intelligence\u003C/h2\u003E\n\u003Cp\u003ESnowflake Intelligence (presto in private preview) è una piattaforma per creare data agent che consentono agli utenti aziendali di analizzare, riassumere e agire a partire da dati strutturati e non strutturati, il tutto in un’unica interfaccia conversazionale unificata. Snowflake Intelligence consente agli utenti di connettersi in modo trasparente ai dati aziendali, come transazioni di vendita, documenti in knowledge base come SharePoint e strumenti per la produttività come Jira e Google Workspace, per generare insight data-driven e agire in linguaggio naturale senza competenze tecniche o di programmazione.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_460253025":{"id":"image-a02e72e2cf","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8a323973-57f9-46dd-b20d-bc8774d88dc6/build2024-ai-ml-snowflake-intelligence1.gif?quality=85&preferwebp=true","alt":"Gif animation showing how Snowflake Intelligence works","lazyEnabled":true,"height":"473","width":"925",":type":"snowflake-site/components/image"},"blog_text_1553733623":{"id":"blog-text-63ab049c9f","text":"\u003Ch1\u003EScopri di più\u003C/h1\u003E\n\u003Cp\u003ECon i più recenti miglioramenti di \u003Ca href=\"https://www.snowflake.com/it/data-cloud/cortex/\" target=\"_blank\"\u003ECortex AI\u003C/a\u003E e \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowflake-ml/\" target=\"_blank\"\u003ESnowflake ML\u003C/a\u003E, gli sviluppatori possono tranquillamente creare app di AI generativa all’interno dell’ambiente sicuro di Snowflake.\u003C/p\u003E\n\u003Cp\u003EInizia subito a creare app AI e modelli personalizzati utilizzando queste risorse:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESoluzioni NLP per pipeline di dati: \u003C/b\u003EProva il quickstart per l’\u003Ca href=\"https://quickstarts.snowflake.com/guide/customer_reviews_analytics_using_snowflake_cortex/index.html?index=..%2F..index#0\" target=\"_blank\"\u003Eanalisi delle recensioni dei clienti utilizzando Snowflake Cortex\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EApp conversazionali:\u003C/b\u003E Crea un chatbot basato su RAG utilizzando il nostro \u003Ca href=\"https://quickstarts.snowflake.com/guide/ask_questions_to_your_own_documents_with_snowflake_cortex_search/index.html?index=..%2F..index#0\" target=\"_blank\"\u003Equickstart\u003C/a\u003E o partecipa alla \u003Ca href=\"http://snowflake-mistral-rag.devpost.com\" target=\"_blank\"\u003ERAG ’n’ Roll Hackathon\u003C/a\u003E per avere la possibilità di vincere $ 10.000 in premi. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EML basato su GPU:\u003C/b\u003E Inizia ad \u003Ca href=\"https://quickstarts.snowflake.com/guide/train-an-xgboost-model-with-gpus-using-snowflake-notebooks/?_ga=2.114671431.1770139711.1729530235-871656794.1699564848&_gac=1.27590350.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\"\u003Eaddestrare un modello XGBoost con GPU\u003C/a\u003E o a \u003Ca href=\"https://quickstarts.snowflake.com/guide/scale-embeddings-with-snowflake-notebooks-on-container-runtime/index.html?index=..%2F..index#0\" target=\"_blank\"\u003Escalare integrazioni personalizzate\u003C/a\u003E da notebook Snowflake.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Ci style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003ENota: \u003Ci style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\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\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_10630711","image_372176798","blog_text_1486098648","image_2080796899","blog_text_1084608810","youtube","blog_text_531091591","image_79610950","blog_text_661965933","image_460253025","blog_text_1553733623"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{"premium_content_bann":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"premium_content_bann":{"id":"premium-content-banner-c56cf6fdc4","contentEyebrow":"Ebook","contentHeadline":"Secrets of Gen AI Success","contentDescription":"Discover how leaders like Bayer and Siemens Energy use gen AI to increase revenue, improve productivity and better serve customers.","showImage":true,"image":{"id":"image","src":"https://www.snowflake.com/content/snowflake-site/global/it/blog/accelerate-ai-development/_jcr_content/root/responsivegrid/container_main_content/flexible_column_container/flexible_column_content_container_1/responsivegrid_premium_content_banner/premium_content_bann/image.coreimg.jpeg/1731396197506/secrets-of-gen-ai-success-blog-1680x720.jpeg","alt":"Photo illustration of two women collaborating near a computer and other images representing tech and 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