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dell’AI",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-e1b5ea83c2","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-3d5fef4b3e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_463561420":{"id":"text-84a5bc94c7","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuida di riferimento\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-b165c78a56","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Osservabilità dell’AI: fiducia e controllo nell’AI in produzione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-d9d3750ef9","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003ECon l’integrazione dei sistemi AI nei workflow critici per il business, le organizzazioni devono poterne osservare e controllare il comportamento. L’osservabilità dell’AI fornisce le tracce, le metriche, le valutazioni e le evidenze di governance necessarie per individuare le cause dei malfunzionamenti, migliorare la qualità e gestire l’AI in produzione con piena affidabilità.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-8234ac8506","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fd9454ea-3b59-4d19-95be-534774cbd226/david.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"David Gaule","isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, 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border-top","layout":"SIMPLE","id":"container-fe13f710d0",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-a22fefa362","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EApprofondimenti sull’osservabilità dell’AI:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-54a6bc07d0","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/model-monitoring/\"\u003EMonitoraggio dei modelli ML\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-44547d319f","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-44b0bdbe39","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EOSSERVABILITÀ DELL’AI: LA DEFINIZIONE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL’osservabilità dell’AI (AI Observability) è la pratica di strumentare, tracciare e valutare i sistemi AI per comprendere in che modo input, dati, prompt, modelli, strumenti e logica applicativa determinano ogni output in produzione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-5b4be2e772","text":"\u003Cp\u003EQuando l’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Eintelligenza artificiale\u003C/a\u003E influenza l’assistenza clienti, l’analisi finanziaria, i workflow di compliance o le decisioni dirigenziali, un output errato rappresenta un rischio aziendale. Eppure, la maggior parte delle implementazioni di enterprise AI offre una visibilità sorprendentemente limitata sui motivi per cui un sistema ha prodotto una determinata risposta o sulle azioni da intraprendere in caso di problemi.\u003C/p\u003E\n\u003Cp\u003EL’osservabilità dell’intelligenza artificiale consente alle organizzazioni di esaminare il comportamento dei sistemi AI dopo che questi escono dall’ambiente controllato del prototipo: in produzione gli output dipendono non solo dal modello, ma anche dai prompt, dalle policy, dalle chiamate ai tool, dalla latenza e dai dati governati disponibili nel momento della richiesta. L’osservabilità consente di comprendere questo comportamento e rispondere alle domande che determinano l’affidabilità di un sistema AI: Che cosa sta facendo realmente il sistema? Perché? Possiamo fidarci?\u003C/p\u003E\n\u003Cp\u003EL’osservabilità trasforma l’AI da una scatola nera in un sistema che può essere osservato, misurato, interrogato e governato in modo continuo e su larga scala. Per le organizzazioni che eseguono l’AI su dati critici per il business, rappresenta la data foundation su cui costruire un’enterprise AI affidabile.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-observability":{"id":"title-v2-2b44da7887","additionalClasses":"anchor-title anchor-title--what-is-ai-observability","type":"heading2","lines":["Che cos’è l’osservabilità dell’AI?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-observability_0":{"id":"text-fd30ee4720","text":"\u003Cp\u003EL’osservabilità dell’AI (AI Observability) è la pratica di strumentare, valutare e tracciare i sistemi AI per comprenderne il comportamento lungo l’intero percorso dall’input all’output. Per un’applicazione di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EAI generativa\u003C/a\u003E, questo percorso può includere il prompt dell’utente, i documenti recuperati, la risposta del modello, le chiamate ai tool, i passaggi intermedi di reasoning, la latenza, l’utilizzo di token, i costi e i punteggi di valutazione. Per un \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/agents/ \"\u003Esistema agentico\u003C/a\u003E, può includere anche la sequenza di azioni eseguite dall’agente, gli strumenti selezionati e i dati a cui ha avuto accesso durante il percorso.\u003C/p\u003E\n\u003Cp\u003EL’osservabilità dell’AI si basa tipicamente su quattro capacità:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003E\u003Cb\u003ETrasparenza\u003C/b\u003E\u003C/a\u003E\u003Cb\u003E:\u003C/b\u003E è necessaria visibilità sul comportamento del sistema AI, per vedere come prompt, contesto recuperato, risposte del modello, chiamate ai tool e logica applicativa interagiscono nel workflow. La visibilità consente di individuare l’origine di un problema e gli interventi necessari per risolverlo.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EMisurabilità:\u003C/b\u003E Le organizzazioni devono poter valutare in modo coerente se gli output dell’AI sono accurati, pertinenti, basati su dati attendibili, sicuri, tempestivi e convenienti per il workflow che supportano. La misurabilità consente di confrontare prompt, modelli, strategie di recupero e configurazioni sulla base di prove, anziché di supposizioni.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EGovernabilità:\u003C/b\u003E I sistemi AI devono operare nel rispetto dei controlli aziendali. Ciò significa rispettare le policy di accesso, utilizzare fonti di dati approvate, registrare le valutazioni e conservare prove di tracing sufficienti per esaminare gli errori. Quando l’AI viene integrata in workflow regolamentati e tratta dati sensibili, l’osservabilità deve diventare parte della \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003Egovernance\u003C/a\u003E, anziché restare un ambito separato dell’ingegneria.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESpiegabilità:\u003C/b\u003E Utenti aziendali, auditor, sviluppatori e dirigenti devono comprendere come è stato prodotto un output prima di potersi fidare. La spiegabilità non richiede di esporre ogni dettaglio interno di un modello, ma impone di mostrare input, contesto, percorso decisionale ed evidenze di valutazione che consentano di analizzare i problemi, correggerli e migliorare il sistema.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-is-ai-observability_0":{"id":"card-v2-bac3cd2769","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: TS Imagine"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/ts-imagine/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3b812ed0-848d-4714-96e8-ce82ea5f8ebb/ts-imagine%25403x.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"TSImagine Logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ETS Imagine utilizza Snowflake e Snowflake Cortex AI per unificare dati, team e tecnologie per oltre 500 clienti del settore finanziario, estendendo i casi d’uso di AI generativa a tutta l’azienda. Con workflow basati su RAG e Streamlit in Snowflake, TS Imagine ha automatizzato il monitoraggio delle email prima svolto manualmente, accelerato il triage dell’assistenza clienti e la ricerca delle conoscenze, ridotto del 30% i costi dell’AI rispetto alle API di LLM esterni e risparmiato 4.000 ore di lavoro manuale all’anno.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_ai-observability-vs-ml-monitoring-vs-model-monitoring":{"id":"title-v2-a2bf4eb503","additionalClasses":"anchor-title anchor-title--ai-observability-vs-ml-monitoring-vs-model-monitoring","type":"heading2","lines":["Osservabilità dell’AI, monitoraggio del ML e monitoraggio dei modelli"],":type":"snowflake-site/components/title-v2"},"text_ai-observability-vs-ml-monitoring-vs-model-monitoring_0":{"id":"text-2bed66a630","text":"\u003Cp\u003EL’osservabilità dell’AI si sovrappone al monitoraggio dei modelli e al monitoraggio del machine learning, ma ha un ambito più ampio.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EIl monitoraggio dei modelli si concentra su un modello distribuito:\u003C/strong\u003E consente di verificare se le previsioni del modello restano accurate, se si è verificato un drift dei dati di input, se la latenza sta cambiando e se il modello continua a rispettare gli obiettivi previsti a livello di servizio. Questo è il fulcro del monitoraggio di molti sistemi di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) supervisionato, soprattutto quelli con una ground truth chiara, come il rilevamento delle frodi, la previsione dell’abbandono dei clienti o la previsione della domanda.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EIl monitoraggio ML estende questa prospettiva all’intera pipeline:\u003C/strong\u003E un modello può deteriorarsi perché le feature arrivano in ritardo, le etichette non sono disponibili nei tempi previsti, le modifiche allo schema interrompono una trasformazione o un processo batch introduce valori mancanti. Il \u003Ca href=\"https://www.snowflake.com/en/fundamentals/observability-vs-monitoring/\"\u003Emonitoraggio ML\u003C/a\u003E tiene sotto controllo il percorso operativo che circonda il modello, tra cui la qualità dei dati, l’aggiornamento delle feature, lo stato della pipeline e il comportamento delle previsioni.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EL’osservabilità dell’AI si estende all’intero sistema AI:\u003C/strong\u003E in un’applicazione basata su un \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Elarge language model\u003C/a\u003E (LLM), il modello è solo uno dei componenti di una catena più ampia. Un’app \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERAG\u003C/a\u003E può recuperare cinque documenti, inserirne tre nel prompt, chiamare un LLM, applicare un guardrail e scrivere la risposta in un workflow. Un agente può pianificare un’attività, chiamare uno strumento di ricerca, interrogare una tabella, riepilogare i risultati, chiamare un altro strumento e infine produrre una raccomandazione. L’osservabilità deve acquisire la traccia, non soltanto la risposta finale.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cem\u003ELeggi la nostra \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/mlops/\"\u003Eguida al MLOps\u003C/a\u003E per scoprire come rendere operativo il ciclo di vita del machine learning, dallo sviluppo e dalla distribuzione dei modelli al monitoraggio e alla gestione in produzione.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-three-pillars-of-observability":{"id":"title-v2-a102a5a409","additionalClasses":"anchor-title anchor-title--the-three-pillars-of-observability","type":"heading2","lines":["I tre pilastri dell’osservabilità"],":type":"snowflake-site/components/title-v2"},"text_the-three-pillars-of-observability_0":{"id":"text-f19a45f8b0","text":"\u003Cp\u003EI pilastri classici dell’osservabilità sono log, metriche e tracce. Si applicano anche ai sistemi AI, ma gli oggetti osservati sono diversi. Oltre agli errori di servizio, all’utilizzo della CPU o alla latenza delle API, è necessario esaminare anche prompt, documenti recuperati, risposte dei modelli, comportamento degli embedding, utilizzo dei token, chiamate agli strumenti, punteggi di aderenza alle fonti e feedback umano.\u003C/p\u003E\n\u003Ch3\u003ELog\u003C/h3\u003E\n\u003Cp\u003EA livello di evento, i log possono acquisire il template del prompt, la query dell’utente, il nome del modello, i metadati della risposta, la query di retrieval, i documenti selezionati, le chiamate agli strumenti, le decisioni relative alle policy, i messaggi di errore e il feedback. In un contesto aziendale, anche questi record richiedono una governance specifica. Prompt e risposte possono contenere dati proprietari, informazioni regolamentate o contesto aziendale interno. La progettazione deve quindi includere conservazione, oscuramento e accesso basato sui ruoli.\u003C/p\u003E\n\u003Cp\u003EUn record di log efficace fornisce dettagli sufficienti per svolgere un’indagine senza trasformare l’osservabilità in un’ulteriore fonte di dati sensibili non gestiti. Per alcuni workflow, ciò può significare archiviare di default i metadati e consentire un accesso limitato al contenuto completo di prompt e risposte durante le indagini autorizzate.\u003C/p\u003E\n\u003Ch3\u003EMetriche\u003C/h3\u003E\n\u003Cp\u003ENel tempo, le metriche indicano se il sistema mantiene prestazioni stabili. Latenza, throughput, tasso di errore, volume delle richieste, utilizzo dei token e costo per richiesta misurano la dimensione operativa. Aderenza alle fonti, pertinenza della risposta, pertinenza del contesto, correttezza fattuale, tasso di rifiuto, tossicità, completezza e coerenza misurano invece la qualità dell’AI.\u003C/p\u003E\n\u003Cp\u003ENei workflow ML, il drift dei dati, il drift delle previsioni e il concept drift consentono di capire se il modello continua a operare in condizioni note. Il Population Stability Index (PSI), l’attribuzione delle feature e i valori SHAP possono supportare questa analisi quando un modello dipende da feature strutturate e risultati noti. Per l’AI generativa, i cui output sono basati sul linguaggio e spesso valutati rispetto al contesto anziché a una singola etichetta, le metriche di valutazione possono misurare quanto una risposta utilizzi correttamente le informazioni recuperate o soddisfi una griglia di valutazione specifica per l’attività.\u003C/p\u003E\n\u003Ch3\u003ETracce\u003C/h3\u003E\n\u003Cp\u003EAll’interno di una singola richiesta, una traccia segue il sistema da un passaggio al successivo. In un chatbot di base, può includere il prompt, la chiamata al modello e la risposta. In un’applicazione RAG, la traccia può includere la domanda originale, la query di retrieval, i chunk ordinati per rilevanza, l’assemblaggio del prompt, la chiamata di inferenza, i risultati della valutazione e la risposta finale. In un workflow agentico, gli span possono comprendere pianificazione, selezione degli strumenti, risposte degli strumenti, nuovi tentativi e output intermedi.\u003C/p\u003E\n\u003Cp\u003EIl valore della traccia consiste nel preservare la sequenza delle operazioni. Se una risposta cita la policy errata, è possibile verificare se è stato recuperato il documento sbagliato, se quello corretto è stato recuperato ma ignorato o se il modello ha generato testo non supportato nonostante avesse ricevuto il contesto corretto. Se un agente impiega 45 secondi per rispondere a una domanda semplice, la traccia può mostrare se il tempo è stato assorbito dall’inferenza del modello, da uno strumento lento, da retrieval ripetuti o da un ciclo nel piano dell’agente.\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EGuarda Anahita Tafvizi, Chief Data & Analytics Officer Snowflake, illustrare l’architettura alla base di una Gen AI affidabile:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_the-three-pillars-of-observability_0":{"id":"embed-7e1435bec4","youtubeVideoId":"gA-R_MgZQAk","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_identifying-ai-drift":{"id":"title-v2-1555be18a0","additionalClasses":"anchor-title anchor-title--identifying-ai-drift","type":"heading2","lines":["Identificare il drift dell’AI"],":type":"snowflake-site/components/title-v2"},"text_identifying-ai-drift_0":{"id":"text-209c560b5a","text":"\u003Cp\u003EQuando i sistemi AI combinano dati strutturati, documenti non strutturati, embedding e contesto di runtime, il drift può manifestarsi in diversi punti.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EData drift:\u003C/strong\u003E I dati di input cambiano nel tempo. Un classificatore per l’assistenza clienti addestrato sui ticket dell’anno precedente può incontrare nuove categorie di prodotti, nuova terminologia o nuovi schemi di escalation.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConcept drift:\u003C/strong\u003E La relazione tra input e risultati cambia. Un modello antifrode può apprendere da un determinato schema di abuso e poi trovarsi di fronte a una nuova tattica che rende meno affidabili le correlazioni precedenti.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrediction drift:\u003C/strong\u003E Le distribuzioni degli output cambiano. Ad esempio, un modello che in precedenza classificava come ad alto rischio il 10% dei casi ora ne classifica così il 35%, sebbene l’azienda non prevedesse una variazione simile.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEmbedding drift:\u003C/strong\u003E Le rappresentazioni vettoriali cambiano al variare dei dati, dei modelli o delle strategie di embedding. In un sistema RAG, questo può influire sui documenti recuperati e sul grado di somiglianza che il sistema rileva tra due contenuti.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDrift della qualità dei dati:\u003C/strong\u003E Valori mancanti, aggiornamenti tardivi, modifiche allo schema e record duplicati incidono sui dati utilizzati dal sistema, anche quando il modello non è cambiato.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-fairness/\"\u003E\u003Cstrong\u003EDrift dell’equità\u003C/strong\u003E\u003C/a\u003E \u003Cstrong\u003Ee dei bias:\u003C/strong\u003E Il comportamento del modello può cambiare in modo diverso tra gruppi di utenti, aree geografiche, linee di prodotto o schemi linguistici. L’osservabilità consente di rilevare se la qualità, il comportamento di rifiuto o i tassi di errore presentano una distribuzione disomogenea.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_identifying-ai-drift_0":{"id":"text-19c2b7afbb","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EL’ERRORE COMUNE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EIl solo monitoraggio non è sufficiente. Il monitoraggio può indicare quando qualcosa è cambiato, ma l’osservabilità fornisce i log, le metriche, le tracce e le valutazioni necessarie per capire perché è cambiato e come correggerlo.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_llm-and-agentic-ai-observability":{"id":"title-v2-6a9d65c751","additionalClasses":"anchor-title anchor-title--llm-and-agentic-ai-observability","type":"heading2","lines":["Osservabilità degli LLM e dell’Agentic AI"],":type":"snowflake-site/components/title-v2"},"text_llm-and-agentic-ai-observability_0":{"id":"text-385712adb1","text":"\u003Cp\u003EL’osservabilità degli LLM si concentra sul comportamento dei sistemi che utilizzano large language model, tra cui chatbot, assistenti documentali, workflow di sintesi, assistenti di programmazione, interfacce text-to-SQL e applicazioni RAG. In questi sistemi, la qualità non dipende soltanto dalla risposta del modello. È necessario sapere se l’applicazione ha recuperato il contesto corretto, composto il prompt appropriato, rispettato i controlli previsti e prodotto una risposta utile, fondata sulle fonti e governata.\u003C/p\u003E\n\u003Ch3\u003ETracciare la pipeline RAG\u003C/h3\u003E\n\u003Cp\u003EIn una pipeline RAG, la risposta prende forma prima che il modello la generi. La query può essere ambigua, il livello di recupero può restituire documenti irrilevanti oppure il prompt può includere il documento corretto ma omettere l’istruzione di citarlo. Il modello può generare una risposta apparentemente plausibile ma non supportata dal contesto recuperato.\u003C/p\u003E\n\u003Cp\u003EUn punteggio di groundedness consente di segnalare questo tipo di errore, mentre la traccia mostra in quale punto è entrato nel sistema. Acquisendo la query, i documenti recuperati, i chunk ordinati per rilevanza, la composizione del prompt, la risposta del modello e il risultato della valutazione, è possibile stabilire se il problema ha avuto origine nel recupero, nella selezione del contesto, nella progettazione del prompt o nel comportamento del modello.\u003C/p\u003E\n\u003Ch3\u003EMonitorare prompt, risposte, costi e latenza\u003C/h3\u003E\n\u003Cp\u003EIl tracciamento di prompt e risposte consente a chi sviluppa di esaminare il percorso della richiesta. Gli span possono acquisire retrieval, chiamate al modello, chiamate ai tool e altre operazioni all’interno di un singolo trace, così è possibile esaminare ogni passaggio senza ricostruire il workflow da log separati.\u003C/p\u003E\n\u003Cp\u003EIl monitoraggio dell’utilizzo dei token aiuta a comprendere costi e performance, in particolare quando un’applicazione invia prompt lunghi, recupera troppo contesto o ripete in loop le chiamate ai tool. Le metriche di latenza mostrano se la lentezza delle risposte dipende dal recupero, dall’inferenza, dall’orchestrazione o da un servizio esterno. Nel complesso, questi segnali consentono di ottimizzare l’applicazione senza considerare qualità, velocità e costi come problemi separati.\u003C/p\u003E\n\u003Ch3\u003EAcquisire i passaggi dell’agente e le chiamate ai tool\u003C/h3\u003E\n\u003Cp\u003EL’osservabilità dell’Agentic AI aggiunge un ulteriore livello, perché gli agenti AI non si limitano a rispondere a un prompt. Un agente AI può eseguire un’intera sequenza di azioni: chiamare uno strumento di ricerca, interrogare una tabella strutturata, aprire un documento, riepilogare i risultati e decidere se porre una domanda di approfondimento.\u003C/p\u003E\n\u003Cp\u003EL’osservabilità deve acquisire questa sequenza per verificare se l’agente AI ha selezionato gli strumenti corretti, consultato i dati appropriati e interrotto l’esecuzione al momento giusto. Senza questa registrazione dettagliata dei singoli passaggi, la risposta finale di un agente AI può sembrare accettabile anche quando il flusso di lavoro sottostante è inefficiente, privo di fondamento o non conforme ai limiti previsti dalle policy.\u003C/p\u003E\n\u003Ch3\u003EStandardizzare la telemetria della Gen AI\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://opentelemetry.io/\" target=\"_blank\"\u003EOpenTelemetry\u003C/a\u003E sta diventando un importante riferimento per la standardizzazione dell’osservabilità di LLM e Agentic AI. Le sue convenzioni semantiche per l’AI generativa definiscono come registrare le operazioni di Gen AI: chiamate al modello, conteggio dei token e, quando le organizzazioni lo abilitano, contenuto dei prompt, completion, chiamate ai tool e relativi risultati.\u003C/p\u003E\n\u003Cp\u003EPoiché le applicazioni AI spesso coinvolgono più framework, modelli e livelli infrastrutturali, un modello telemetrico comune è essenziale per confrontare i comportamenti tra sistemi, riutilizzare gli schemi di strumentazione ed evitare uno stack di osservabilità frammentato.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-implement-ai-observability":{"id":"title-v2-792d8baf12","additionalClasses":"anchor-title anchor-title--how-to-implement-ai-observability","type":"heading2","lines":["Come implementare l’osservabilità dell’AI"],":type":"snowflake-site/components/title-v2"},"text_how-to-implement-ai-observability_0":{"id":"text-f43cf2a7c0","text":"\u003Cp\u003EPrima di aggiungere la strumentazione a un’applicazione AI, occorre stabilire che cosa il sistema deve dimostrare in produzione. Ad esempio, un assistente per l’assistenza clienti può dover dimostrare che la risposta deriva da contenuti aggiornati delle policy, mentre un assistente finanziario può dover indicare quali tabelle, definizioni delle metriche e periodi di rendicontazione hanno determinato la spiegazione di uno scostamento.\u003C/p\u003E\n\u003Cp\u003EI requisiti del flusso di lavoro determinano quali informazioni acquisire. Nei flussi di lavoro a basso rischio possono essere sufficienti feedback essenziali, tracce campionate e controlli di qualità di base. Nei workflow regolamentati o critici per l’azienda, l’applicazione richiede in genere un tracciamento più approfondito, set di dati di valutazione più rigorosi, record con accesso controllato e criteri chiari per il passaggio in produzione, prima che una modifica al prompt, al modello o al retrieval raggiunga l’ambiente di produzione.\u003C/p\u003E\n\u003Ch3\u003EStrumentare il percorso di esecuzione con la telemetria\u003C/h3\u003E\n\u003Cp\u003ELa strumentazione acquisisce i log, le metriche e le tracce che rendono osservabile il sistema. Per i sistemi AI, ciò significa strumentare il percorso dell’applicazione attorno al modello: prompt, retrieval, chiamate agli strumenti, risposte del modello, punteggi di valutazione, latenza, utilizzo dei token e feedback degli utenti.\u003C/p\u003E\n\u003Cp\u003EPer un’applicazione RAG, sono inclusi il prompt dell’utente, la query di retrieval, il contesto recuperato, la costruzione del prompt, la risposta del modello, i punteggi di valutazione, la latenza e i costi. Per un agente comprende anche la pianificazione, le chiamate ai tool, i risultati intermedi, i tentativi ripetuti e le condizioni di arresto.\u003C/p\u003E\n\u003Ch3\u003EDefinire baseline e avvisi\u003C/h3\u003E\n\u003Cp\u003EQuando tracce e metriche iniziano ad affluire, le baseline definiscono un intervallo normale per qualità e prestazioni, che comprende latenza, costi, fondatezza, tasso di rifiuto, pertinenza delle risposte, metriche di drift e altri segnali. Poiché variano in base al workflow, le baseline devono riflettere l’attività anziché uno standard generico.\u003C/p\u003E\n\u003Cp\u003EGli avvisi devono concentrarsi sui cambiamenti significativi, non su ogni deviazione. Un lieve aumento dell’utilizzo dei token dopo l’aggiunta di un contesto più ricco può essere previsto, mentre un calo improvviso della fondatezza dopo la modifica di un prompt richiede un’indagine.\u003C/p\u003E\n\u003Ch3\u003ESviluppare cicli di feedback\u003C/h3\u003E\n\u003Cp\u003EL’osservabilità dell’AI deve alimentare il miglioramento. I risultati delle valutazioni possono orientare le modifiche ai prompt, il tuning del retrieval, la selezione dei modelli, le modifiche ai guardrail e la correzione dei problemi di qualità dei dati. Il feedback degli utenti può contribuire a individuare lacune nei dati di test. L’analisi delle tracce può mostrare dove un agente AI ripete un’attività, sceglie lo strumento sbagliato o non utilizza il contesto recuperato.\u003C/p\u003E\n\u003Cp\u003EQuesto ciclo è particolarmente importante per l’AI generativa, perché la qualità viene spesso valutata per confronto. È necessario sapere se un nuovo prompt, modello, strategia di embedding o configurazione di inferenza ha migliorato l’applicazione per l’attività prevista. L’osservabilità dell’AI Snowflake supporta il confronto affiancato delle valutazioni tra LLM, prompt e configurazioni di inferenza, consentendo di valutare la qualità delle risposte prima di promuovere una configurazione in produzione.\u003C/p\u003E\n\u003Ch3\u003EStandardizzare dove possibile\u003C/h3\u003E\n\u003Cp\u003ECon la diffusione delle app AI tra i diversi team, l’osservabilità può frammentarsi rapidamente. Un team registra i prompt in un database applicativo, un altro monitora i costi in una dashboard e un altro ancora conserva i risultati delle valutazioni nei notebook. La standardizzazione della telemetria, dei set di dati di valutazione, delle metriche e dei formati delle tracce consente all’organizzazione di confrontare i sistemi e riutilizzare i modelli operativi.\u003C/p\u003E\n\u003Cp\u003EOpenTelemetry supporta il livello di telemetria, mentre le piattaforme dati governate supportano i dati di valutazione, i controlli degli accessi, i record di monitoraggio e l’archiviazione delle tracce. L’obiettivo non è imporre la stessa progettazione a ogni applicazione AI. L’obiettivo è garantire che ogni sistema AI in produzione generi prove sufficienti per valutarlo e migliorarlo.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-implement-ai-observability_0":{"id":"text-abc86147f0","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003ECONSIGLIO RAPIDO\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EStrumenta fin dall’inizio l’intero percorso di esecuzione, inclusi prompt, retrieval, chiamate agli strumenti, risposte del modello, punteggi di valutazione, latenza, utilizzo dei token e feedback, per disporre delle prove necessarie a risolvere i problemi, confrontare le configurazioni e governare l’AI in produzione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_why-ai-observability-on-snowflake":{"id":"title-v2-61904c5c44","additionalClasses":"anchor-title anchor-title--why-ai-observability-on-snowflake","type":"heading2","lines":["Perché l’osservabilità dell’AI su Snowflake"],":type":"snowflake-site/components/title-v2"},"text_why-ai-observability-on-snowflake_0":{"id":"text-cbce24ff19","text":"\u003Cp\u003ENell’enterprise AI, i dati utilizzati per generare una risposta e le prove necessarie a valutarla devono rimanere strettamente collegati. Quando dati di origine, prompt, tracce, valutazioni e policy di accesso risiedono in sistemi disconnessi, ogni indagine inizia ricostruendo quale versione è stata eseguita, quale contesto è stato recuperato, quale policy è stata applicata e quale configurazione è cambiata.\u003C/p\u003E\n\u003Cp\u003EAll’interno dell’AI Data Cloud Snowflake, \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ESnowflake Cortex AI\u003C/a\u003E offre accesso a funzionalità di AI generativa basate su dati aziendali governati, mentre la Snowflake AI Observability supporta la valutazione, il confronto e il tracciamento sistematici di app e agenti AI generativi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_building-trust-through-observable-ai":{"id":"title-v2-b5e60e573e","additionalClasses":"anchor-title anchor-title--building-trust-through-observable-ai","type":"heading2","lines":["Creare fiducia attraverso un’AI osservabile"],":type":"snowflake-site/components/title-v2"},"text_building-trust-through-observable-ai_0":{"id":"text-48bbe2736b","text":"\u003Cp\u003ECon la crescente integrazione dei sistemi AI nei processi aziendali, la fiducia dipende dalla capacità dell’organizzazione di esaminare il percorso alla base di ogni output. Una traccia, un punteggio di valutazione, un documento recuperato e una configurazione del modello non sono semplici dettagli tecnici da esaminare a posteriori. Sono i record che consentono di migliorare i sistemi AI senza perdere il controllo dei dati, delle policy e dei workflow da cui dipendono.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_building-trust-through-observable-ai_0":{"id":"text-4c8a3dd93b","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EIN SINTESI\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL’osservabilità dell’AI offre la visibilità necessaria per fidarsi dell’AI in produzione, tracciando come prompt, dati, contesto recuperato, modelli, chiamate ai tool e logica applicativa determinano ciascun output.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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