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learning",":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-a55d8bf1b6","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-280a4ad078",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_470483971":{"id":"text-07becde922","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-v2-4b81cfb013","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Il machine learning in azienda: ciclo di vita dell’ML e best practice"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-7b93e9bf50","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EIl machine learning amplia le possibilità di utilizzo dei dati già raccolti dalle organizzazioni. Questa guida illustra il ciclo di vita dell’ML, le principali best practice e l’infrastruttura dati necessaria ai team per sviluppare, distribuire e monitorare l’ML in modo affidabile.\u003C/p\u003E\n","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,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-8529a47e2c",":type":"snowflake-site/components/container",":items":{"content_chip_copy":{"id":"content-chip-2b7947ff4c","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie Macpherson headshot","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip":{"id":"content-chip-5b467c5373","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--af497c82-10f0-4f45-805e-7f1c4677a824/david.jpg?quality=85&preferwebp=true","alt":"David Gaule","lazyEnabled":true,"isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip_copy","content_chip"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["text_470483971","title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-6123761a2c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-a9bff8c913","youtubeVideoId":"mhjjtTF35V0","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"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-db61abc613","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-ecbd0557a6",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-0411ad125e","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EApprofondimenti sul machine learning:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-dc714d1851","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/automl/\"\u003EAutoML\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/deep-learning/\"\u003EDeep learning\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/ensemble-learning/\"\u003EApprendimento ensemble\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/feature-engineering/\"\u003EFeature engineering\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/frameworks/\"\u003EFramework ML\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/inference/\"\u003EInferenza ML\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/models/\"\u003EModelli ML\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/reinforcement-learning/\"\u003EApprendimento per rinforzo\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-bb8d27bbf8","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_what-is-ai-governance_0":{"id":"text-a5efd169f2","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EMACHINE LEARNING: LA DEFINIZIONE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EIl machine learning (ML) è una branca dell’intelligenza artificiale che consente ai sistemi di individuare pattern nei dati e utilizzarli per generare previsioni, raccomandazioni e altri output. Anziché basarsi esclusivamente su regole programmate manualmente, l’ML consente alle organizzazioni di automatizzare le decisioni, ricavare insight e migliorare i risultati con l’aumentare dei dati disponibili.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_intro_0":{"id":"text-50f54e3f35","text":"\u003Cp\u003EIl machine learning è passato dai progetti di ricerca e dagli esperimenti isolati di data science ai sistemi aziendali che instradano le transazioni, personalizzano le esperienze dei clienti, rilevano anomalie di sicurezza, prevedono la domanda e supportano le decisioni quotidiane. Il suo valore consiste nell’utilizzare i dati per riconoscere relazioni troppo vaste, dinamiche o complesse perché le persone possano codificarle manualmente.\u003C/p\u003E\n\u003Cp\u003EMettere in produzione il machine learning richiede più della semplice scelta di un algoritmo. I team hanno bisogno di dati affidabili, pipeline di feature ripetibili, criteri di valutazione chiari, controlli sulla distribuzione e un monitoraggio che verifichi se il modello continua a comportarsi come previsto dopo un cambiamento delle condizioni aziendali. \u003C/p\u003E\n\u003Cp\u003EQuesta guida spiega come funziona il machine learning, le best practice per il ciclo di vita dell’ML e gli aspetti che occorre considerare quando si implementano sistemi ML.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-governance":{"id":"title-v2-e15ecf1dd3","additionalClasses":"anchor-title anchor-title--what-is-machine-learning","type":"heading2","lines":["Che cos’è il machine learning?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-governance_0":{"id":"text-f325c9cd31","text":"\u003Cp\u003EIl machine learning è un sottoinsieme dell’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Eintelligenza artificiale\u003C/a\u003E in cui il software apprende pattern dai dati e li utilizza per generare previsioni, classificazioni, raccomandazioni o altri output. Nella programmazione tradizionale basata su regole, chi sviluppa il software scrive istruzioni esplicite: se si verifica questa condizione, esegui questa azione. Nel machine learning, il sistema utilizza i dati di addestramento per dedurre una relazione statistica tra input e output, quindi applica la relazione appresa a nuovi dati durante l’inferenza. \u003C/p\u003E\n\u003Cp\u003EAd esempio, un modello ML potrebbe apprendere che determinati attributi delle transazioni sono associati alle frodi, che specifici pattern di utilizzo anticipano il churn o che la combinazione tra storico delle vendite e stagionalità migliora le previsioni della domanda. L’algoritmo definisce il modo in cui il sistema apprende, mentre il modello è l’artefatto appreso prodotto dall’addestramento. L’inferenza consiste nell’applicare il modello a nuovi dati.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ai-governance-matters-now":{"id":"title-v2-801a3aba35","additionalClasses":"anchor-title anchor-title--why-machine-learning-matters-for-enterprise-data-teams","type":"heading2","lines":["Perché il machine learning è importante per i team aziendali che lavorano con i dati"],":type":"snowflake-site/components/title-v2"},"text_why-ai-governance-matters-now_0":{"id":"text-c787b1eba1","text":"\u003Cp\u003EIl machine learning amplia le possibilità di utilizzo dei dati già raccolti dalle organizzazioni. Può elaborare volumi di informazioni che le persone non riescono a esaminare manualmente e individuare segnali prima di una dashboard concepita per l’analisi retrospettiva. Può inoltre automatizzare decisioni ripetitive che seguono pattern riconoscibili.\u003C/p\u003E\n\u003Cp\u003ELa difficoltà è che l’ML in produzione cambia il ruolo della piattaforma dati: i dati non alimentano più soltanto report statici, ma determinano anche le operazioni aziendali. I problemi di qualità dei dati possono trasformarsi in errori di previsione e quindi in decisioni inadeguate, una volta integrate le previsioni nei workflow aziendali.\u003C/p\u003E\n\u003Cp\u003EUn modello ML dipende da tabelle, eventi, etichette, feature, policy di accesso e schemi di aggiornamento. Gli input frammentati o con una governance inadeguata non interrompono il processo di addestramento, ma producono risultati difficili da verificare, riprodurre o eseguire in modo affidabile in produzione.\u003C/p\u003E\n\u003Cp\u003EPer questo, i programmi ML aziendali avanzano in genere solo nei limiti consentiti dalla loro infrastruttura dati. I team hanno bisogno di dati governati, pipeline ripetibili, controlli sul ciclo di vita dei modelli e strumenti per monitorare se il comportamento dei modelli in produzione corrisponde alle condizioni in cui sono stati addestrati.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001":{"id":"title-v2-50ce191535","additionalClasses":"anchor-title anchor-title--how-machine-learning-works-ml-lifecycle","type":"heading2","lines":["Come funziona il machine learning: il ciclo di vita dell’ML"],":type":"snowflake-site/components/title-v2"},"text_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001_0":{"id":"text-38736e8187","text":"\u003Cp\u003ELa maggior parte dei sistemi di machine learning segue un ciclo di vita iterativo: preparare i dati, sviluppare le feature, addestrare un modello, valutarne le prestazioni, distribuirlo in una pipeline di inferenza e monitorarlo nel tempo. \u003C/p\u003E\n\u003Ch3\u003EPreparazione dei dati\u003C/h3\u003E\n\u003Cp\u003EIl ciclo di vita inizia generalmente con la preparazione dei dati. I team raccolgono dati grezzi, correggono i valori incoerenti, gestiscono i dati mancanti, eseguono il join delle tabelle pertinenti e creano un set di dati di addestramento che rappresenta il problema che il modello deve risolvere. In un modello di abbandono dei clienti (churn) ad esempio, il set di dati di addestramento potrebbe includere attributi dell’account, utilizzo del prodotto, interazioni con l’assistenza, cronologia di fatturazione e un’etichetta che indica se il cliente ha rinnovato il contratto.\u003C/p\u003E\n\u003Ch3\u003EFeature engineering\u003C/h3\u003E\n\u003Cp\u003ELa feature engineering converte i dati preparati in segnali dai quali un modello può apprendere, trasformando timestamp grezzi, sequenze di transazioni o testo non strutturato in valori derivati come finestre temporali di recenza, medie mobili o embedding. Questo passaggio richiede spesso la conoscenza del dominio, perché il segnale utile raramente coincide con la sola colonna grezza: conta piuttosto il comportamento del valore nel tempo, tra diversi account o in relazione al risultato che il modello cerca di prevedere. Ad esempio, in un modello di churn, il numero totale di accessi è meno significativo di un calo improvviso dell’utilizzo dopo l’onboarding. In un modello di rilevamento delle frodi, l’importo di una transazione non ha significato senza sapere se è anomalo per l’account, l’esercente o la località.\u003C/p\u003E\n\u003Ch3\u003EAddestramento del modello\u003C/h3\u003E\n\u003Cp\u003EDurante l’addestramento del modello, l’algoritmo regola i parametri interni per ridurre l’errore rispetto a un obiettivo di addestramento. In molti modelli, tale obiettivo è rappresentato da una funzione di perdita, mentre i metodi di ottimizzazione dell’addestramento, come la discesa del gradiente, regolano i pesi del modello per ridurre la perdita. I data scientist ottimizzano anche gli iperparametri, come il tasso di apprendimento, la profondità degli alberi o l’intensità della regolarizzazione, che determinano il modo in cui il modello apprende ma non vengono appresi direttamente dai dati.\u003C/p\u003E\n\u003Ch3\u003EValutazione\u003C/h3\u003E\n\u003Cp\u003ELa valutazione verifica se il modello è in grado di generalizzare oltre i dati di addestramento. I team utilizzano generalmente un set di convalida o un processo di convalida incrociata per confrontare i modelli candidati, verificare la presenza di overfitting e misurare le prestazioni con metriche come precision, recall, punteggio F1, radice dell’errore quadratico medio (RMSE) o area sotto la curva, a seconda del caso d’uso.\u003C/p\u003E\n\u003Ch3\u003EImplementazione\u003C/h3\u003E\n\u003Cp\u003EIl deployment inserisce il modello in una pipeline di inferenza. Nell’inferenza batch, il modello assegna un punteggio ai record secondo una pianificazione, ad esempio generando ogni notte previsioni sull’abbandono o ogni settimana previsioni della domanda. Nell’inferenza in tempo reale, il modello restituisce una previsione durante un workflow attivo, ad esempio assegnando un punteggio a una transazione mentre il cliente sta ancora completando l’acquisto.\u003C/p\u003E\n\u003Ch3\u003EMonitoraggio\u003C/h3\u003E\n\u003Cp\u003EIl monitoraggio chiude il ciclo. Un modello che ha ottenuto buoni risultati durante la valutazione può essere soggetto a drift in produzione quando cambiano gli input o le etichette oppure si interrompono le pipeline a monte. La documentazione di ML Observability Snowflake rileva che il comportamento dei modelli può cambiare nel tempo a causa del drift degli input, di ipotesi di addestramento obsolete, di problemi nelle pipeline di dati e di variazioni nei modelli di traffico.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_why_ai_gover":{"id":"card-v2-a38d2b96fc","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EPenske Logistics utilizza Snowflake e Snowflake Cortex AI per sfruttare in modo sicuro l’AI generativa, migliorare l’efficienza operativa e fornire ai manager informazioni più chiare sulla sicurezza, sulle performance e sulla retention del personale. Centralizzando i dati in Snowflake, Penske genera report di BI basati su cinque anni di dati aziendali in soli 15 minuti e ha creato un nuovo modello AI di riepilogo in meno di 15 giorni.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer Story: Penske"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/penske/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--45e02db9-cdb8-453c-bd20-8b8d72be4fd5/penske.png?quality=85&preferwebp=true","alt":"penske logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card"},"title_key-components-of-ai-governance":{"id":"title-v2-2e1799c8db","additionalClasses":"anchor-title anchor-title--types-of-machine-learning","type":"heading2","lines":["Tipi di machine learning"],":type":"snowflake-site/components/title-v2"},"text_key-components-of-ai-governance_0":{"id":"text-f2895f9af6","text":"\u003Cp\u003EI metodi ML sono spesso raggruppati in base al modo in cui il modello apprende dai dati.\u003C/p\u003E\r\n\u003Ch3\u003EApprendimento supervisionato\u003C/h3\u003E\r\n\u003Cp\u003EL’apprendimento supervisionato utilizza dati etichettati per apprendere la relazione tra input e output noti. È comune nel machine learning aziendale perché molti problemi di business dispongono di etichette storiche, come frode o assenza di frode, abbandono o rinnovo, approvazione o rifiuto e domanda prevista o effettiva. L’apprendimento supervisionato comprende la classificazione, che prevede categorie, e la regressione, che prevede valori numerici.\u003C/p\u003E\r\n\u003Ch3\u003EApprendimento non supervisionato \u003C/h3\u003E\r\n\u003Cp\u003EL’apprendimento non supervisionato individua schemi nei dati privi di etichette. Il clustering può raggruppare clienti con comportamenti simili, mentre la riduzione della dimensionalità può comprimere dati complessi in una rappresentazione più piccola per l’analisi o la modellazione a valle. IBM distingue questo metodo dall’apprendimento supervisionato osservando che i modelli non supervisionati si adattano autonomamente a un set di dati senza esempi etichettati.\u003C/p\u003E\r\n\u003Ch3\u003EApprendimento per rinforzo\u003C/h3\u003E\r\n\u003Cp\u003EL’apprendimento per rinforzo addestra un \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/agents/\"\u003Eagente AI\u003C/a\u003E attraverso l’interazione con un ambiente. Anziché apprendere da un set di dati etichettato e fisso, l’agente riceve ricompense o penalità quando esegue azioni e migliora la propria policy nel tempo. IBM descrive l’apprendimento per rinforzo come un addestramento per tentativi ed errori utilizzato in ambiti come la robotica, i giochi e i modelli di ragionamento.\u003C/p\u003E\r\n\u003Ch3\u003EApprendimento semi-supervisionato\u003C/h3\u003E\r\n\u003Cp\u003EL’apprendimento semi-supervisionato combina una quantità ridotta di dati etichettati con un insieme più ampio di dati non etichettati, una soluzione utile quando ottenere le etichette è costoso o richiede molto tempo.\u003C/p\u003E\r\n\u003Ch3\u003EApprendimento auto-supervisionato\u003C/h3\u003E\r\n\u003Cp\u003EL’apprendimento auto-supervisionato crea segnali di addestramento dai dati stessi, un metodo diventato particolarmente importante per i \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model\"\u003Emodelli linguistici di grandi dimensioni\u003C/a\u003E (LLM) e altri approcci basati su modelli fondazionali. Il National Institute of Standards and Technology (NIST) osserva che i modelli foundation a duplice uso vengono generalmente addestrati su ampie raccolte di dati e utilizzano spesso l’auto-supervisione. Nell’Unione europea e in Italia, il \u003Ca href=\"https://eur-lex.europa.eu/eli/reg/2024/1689/oj?locale=it\" target=\"_blank\"\u003Eregolamento UE 2024/1689\u003C/a\u003E (AI Act) indica che i modelli di intelligenza artificiale per finalità generali, generalmente addestrati su grandi quantità di dati con metodi quali l’apprendimento autosupervisionato, non supervisionato o per rinforzo.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_responsible-ai-principles-that-operationalize-governance":{"id":"title-v2-6084674342","additionalClasses":"anchor-title anchor-title--deep-learning-and-neural-networks","type":"heading2","lines":["Deep learning e reti neurali"],":type":"snowflake-site/components/title-v2"},"text_responsible-ai-principles-that-operationalize-governance_0":{"id":"text-7cbe2722e0","text":"\u003Cp\u003EIl deep learning è un tipo di machine learning che utilizza reti neurali multistrato per apprendere rappresentazioni dai dati. Una rete neurale contiene livelli di nodi connessi, pesi regolati durante l’addestramento e funzioni di attivazione che consentono alla rete di modellare relazioni non lineari. La retropropagazione trasmette a ritroso nella rete le informazioni sull’errore, affinché il modello possa aggiornare i propri pesi e migliorare le prestazioni.\u003C/p\u003E\n\u003Cp\u003ELe diverse architetture di rete neurale sono adatte a tipi di dati e task differenti: \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ELe reti neurali convoluzionali (CNN o ConvNet) vengono spesso utilizzate per le immagini e la visione artificiale.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EI transformer sono ampiamente utilizzati per il linguaggio, la modellazione di sequenze e l’\u003Ca href=\"https://www.snowflake.com/it/fundamentals/generative-ai-architecture-models-applications/\"\u003EAI generativa\u003C/a\u003E. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ELe reti neurali a grafo possono modellare le relazioni tra entità connesse, come utenti, account, dispositivi o prodotti. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EGli autoencoder possono comprimere i dati in rappresentazioni a dimensionalità inferiore e ricostruirli, risultando utili per il rilevamento delle anomalie e la compressione.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EIl deep learning può produrre validi risultati quando i team dispongono di dati, capacità di calcolo e disciplina operativa sufficienti. Evidenzia inoltre l’importanza delle decisioni infrastrutturali: addestrare una rete neurale di grandi dimensioni può richiedere accelerazione tramite GPU, addestramento distribuito, monitoraggio degli esperimenti e un attento controllo dei costi.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-transparency-and-explainability":{"id":"title-v2-938f9f11d4","additionalClasses":"anchor-title anchor-title--model-training-and-optimization","type":"heading2","lines":["Addestramento e ottimizzazione dei modelli"],":type":"snowflake-site/components/title-v2"},"text_ai-transparency-and-explainability_0":{"id":"text-f69bb0e939","text":"\u003Cp\u003EL’addestramento di un modello non consiste soltanto nell’adattarlo ai dati. L’obiettivo è sviluppare un modello capace di generalizzare: deve ottenere buoni risultati su nuovi esempi che rispecchiano le condizioni reali che incontrerà in produzione. Un modello che memorizza i dati di addestramento può apparire efficace durante lo sviluppo, ma fallire quando incontra nuovi clienti, nuove transazioni o nuove condizioni di mercato.\u003C/p\u003E\n\u003Cp\u003ELe tecniche di ottimizzazione aiutano i team a gestire questo compromesso:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ELa regolarizzazione impedisce ai modelli di diventare eccessivamente complessi.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ELa convalida incrociata verifica le prestazioni su più suddivisioni dei dati. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EI metodi ensemble combinano più modelli, come gli alberi decisionali in un modello random forest o gradient boosting, per migliorare le prestazioni predittive e la stabilità. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EIl transfer learning parte da un modello addestrato su un task o un set di dati e lo adatta a un altro. Il fine‑tuning è un metodo comune: prosegue l’addestramento di un modello preaddestrato su dati nuovi o più specifici per adattarne il comportamento a un determinato caso d’uso.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECon l’aumentare delle dimensioni dei modelli, l’addestramento comporta implicazioni infrastrutturali e di costo. Un modello di piccole dimensioni può essere addestrato su una singola macchina, mentre un modello di deep learning o un modello foundation può richiedere GPU, più memoria e tempi di esecuzione più lunghi. \u003C/p\u003E\n\u003Cp\u003EI team possono gestire questa complessità con l’addestramento distribuito, che ripartisce il processo tra più dispositivi o nodi; la precisione mista, che riduce l’uso della memoria impiegando formati numerici più piccoli quando opportuno; e metodi di fine‑tuning efficienti nell’uso dei parametri, come LoRA, che inseriscono piccole matrici a basso rango accanto ai pesi originali del modello, addestrando solo queste aggiunte e mantenendo invariati i pesi originali, anziché aggiornare l’intero modello.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_why-ai-governance-on-snowflake_0":{"id":"card-v2-ed54b8cb31","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EHelloFresh utilizza l’AI Data Cloud Snowflake come piattaforma dati centralizzata per unificare fonti di dati frammentate, accelerare l’ottenimento di insight e supportare decisioni in tempo reale nello sviluppo dei menu, nel pricing, nelle promozioni, nelle operazioni di magazzino e nella gestione della supply chain. HelloFresh ha sostituito gli strumenti di analisi dei dati di terze parti con dati di prima parte sulle interazioni con i clienti, migliorando la personalizzazione, rafforzando la conformità alle norme sulla privacy e riducendo i costi del 30%.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer story: HelloFresh"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/it/customers/all-customers/case-study/hellofresh/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8b61682d-6f80-444b-a719-217ce77798f2/hello-fresh.png?quality=85&preferwebp=true","alt":"hello fresh logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card"},"title_ai-traceability-and-auditability":{"id":"title-v2-a1812d3ade","additionalClasses":"anchor-title anchor-title--mlops-managing-models-in-production","type":"heading2","lines":["MLOps: gestire i modelli in produzione"],":type":"snowflake-site/components/title-v2"},"text_ai-traceability-and-auditability_0":{"id":"text-340d93ea3e","text":"\u003Cp\u003ELe operazioni di machine learning (MLOps) rendono il ciclo di vita dell’ML ripetibile, governato e affidabile quando i modelli entrano a far parte dei sistemi di produzione. Offrono ai team un modo per gestire le versioni dei modelli, i workflow di deployment, l’infrastruttura di serving, il monitoraggio, il riaddestramento e le procedure di rollback, evitando che un modello dipenda da attività una tantum nei notebook o da passaggi di consegne non documentati.\u003C/p\u003E\n\u003Cp\u003EÈ in questa fase che emerge più spesso la distanza tra un modello promettente e un sistema ML in produzione. I team di produzione devono comunque sapere quale versione del modello genera le previsioni, quali dati e feature l’hanno determinata, quali metriche di valutazione ne hanno giustificato il rilascio, chi ha approvato il deployment e come si comporta il modello quando cambiano le condizioni aziendali. Senza controlli MLOps, è difficile riprodurre i risultati, analizzare gli errori o stabilire se un modello debba essere riaddestrato, ripristinato a una versione precedente o ritirato.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy":{"id":"text-bd13c6e89f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EL’ERRORE COMUNE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EMolte organizzazioni si concentrano soprattutto sulla scelta del modello, sottovalutando le difficoltà operative legate alla qualità dei dati, alla coerenza delle feature, alla governance e al monitoraggio. Un modello può ottenere buoni risultati durante lo sviluppo, ma peggiorare rapidamente in produzione se cambiano i dati upstream, i presupposti di addestramento diventano obsoleti o le pipeline di inferenza non vengono monitorate in termini di drift e affidabilità.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_ai_traceability":{"id":"text-c1243a14d6","text":"\u003Cp\u003ELe pratiche MLOps più comuni includono:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EControllo delle versioni dei modelli:\u003C/b\u003E tiene traccia di ogni artefatto del modello, dei relativi dati di addestramento, delle definizioni delle feature, dei parametri, delle dipendenze e dei risultati della valutazione.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDeployment dei modelli:\u003C/b\u003E sposta i modelli approvati nei workflow di inferenza batch o in tempo reale, applicando chiari controlli di rilascio.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EServing dei modelli:\u003C/b\u003E fornisce l’infrastruttura che consente ad app, pipeline o analisti di richiedere previsioni al modello.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EExperiment Tracking:\u003C/b\u003E registra le esecuzioni di training, le metriche e le scelte di configurazione per consentire ai team di confrontare i modelli e riprodurre i risultati.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EMonitoraggio dei modelli:\u003C/b\u003E monitora prestazioni, drift, volume dei dati e altri segnali che indicano se il modello continua a comportarsi come previsto.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETraining continuo: \u003C/b\u003Eripete il training o aggiorna i modelli quando nuovi dati, drift o cambiamenti aziendali rendono meno affidabile il modello esistente.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EMLOps gestisce il ciclo di vita come sistema operativo. Collega il lavoro di data science ai requisiti di produzione di software engineering, data engineering, governance e responsabilità aziendale. Un model registry, un feature store, un workflow CI/CD, un layer di monitoraggio e un processo di approvazione consentono di rispondere alle stesse domande fondamentali: quale modello è in esecuzione, quali elementi lo hanno determinato e l’organizzazione può fidarsi del suo comportamento attuale?\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EScopri come l’AI Data Cloud Snowflake semplifica e accelera l’utilizzo dei dati, consentendo alle aziende di sfruttare l’AI su larga scala:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube":{"id":"embed-8eafdbd953","youtubeVideoId":"xXpqQk0CBZY","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_training-data-governance-and-bias-auditing":{"id":"title-v2-9e4bb48442","additionalClasses":"anchor-title anchor-title--machine-learning-on-snowflake","type":"heading2","lines":["Machine learning su Snowflake"],":type":"snowflake-site/components/title-v2"},"text_training-data-governance-and-bias-auditing_0":{"id":"text-421582dda5","text":"\u003Cp\u003EIl machine learning è più facile da rendere operativo quando dati, controlli di governance, workflow di sviluppo e ciclo di vita dei modelli rimangono strettamente integrati. \u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E offre funzionalità che supportano molte fasi dei workflow ML in Snowflake, tra cui preparazione dei dati, feature engineering, training dei modelli, inferenza e monitoraggio. \u003C/p\u003E\n\u003Cp\u003E“Snowflake ML offre funzionalità che supportano molte fasi dei workflow ML in Snowflake, tra cui preparazione dei dati, feature engineering, training dei modelli, inferenza e monitoraggio”.\u003C/p\u003E\n\u003Cp\u003ELe principali funzionalità di Snowflake ML includono:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESnowpark ML: \u003C/b\u003EAPI native per Python per preprocessing, feature engineering, training e deployment all’interno di Snowflake.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESnowflake Feature Store:\u003C/b\u003E un ambiente governato in cui creare, gestire, riutilizzare e mettere a disposizione le feature ML in Snowflake.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESnowflake Model Registry:\u003C/b\u003E un sistema centrale per gestire modelli, metadati, versioni e workflow di inferenza.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EML Observability: \u003C/b\u003Emonitoraggio delle prestazioni, del drift e del volume dei modelli dopo il deployment.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EContainer Runtime for ML:\u003C/b\u003E ambienti CPU e GPU preconfigurati per sperimentazione, training, tuning, inferenza batch e fine‑tuning su Snowpark Container Services.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESnowflake ML Jobs: \u003C/b\u003Euna soluzione per eseguire workflow ML nei runtime container di Snowflake ML da diversi ambienti di sviluppo.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ENotebooks on Container Runtime: \u003C/b\u003Eambienti di sviluppo interattivi adatti al training dei modelli e al tuning dei parametri, con i pacchetti ML più diffusi preinstallati.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EIl vantaggio architetturale è chiaro: i team possono sviluppare e rendere operativo il ML dove risiedono già i dati governati. Una pipeline di feature può leggere dati da tabelle controllate, un processo di training può utilizzare il compute Snowflake, un modello può essere registrato nel registry, l’inferenza può essere eseguita sui dati governati e il monitoraggio può rilevare eventuali cambiamenti nel comportamento in produzione nel tempo. \u003C/p\u003E\n\u003Cp\u003EL’architettura non elimina la necessità di competenze di data science, verifiche di governance o gestione del rischio dei modelli, ma offre ai team una base più coerente per passare dalla sperimentazione alla produzione.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_generative-ai-and-ai-agent-governance":{"id":"title-v2-8bb2bc49f7","additionalClasses":"anchor-title anchor-title--production-ml-starts-with-data-foundation","type":"heading2","lines":["Il ML in produzione parte dall’infrastruttura dati"],":type":"snowflake-site/components/title-v2"},"text_generative-ai-and-ai-agent-governance_0":{"id":"text-08d09d7ac9","text":"\u003Cp\u003EIl machine learning trasforma i dati in previsioni e decisioni automatizzate, ma il modello è solo una parte del sistema. Gli elementi che lo circondano sono altrettanto importanti: i dati di training devono essere affidabili, le feature definite in modo coerente e i modelli valutati prima della produzione e monitorati dopo, perché dati, utenti e condizioni aziendali cambiano.\u003C/p\u003E\n\u003Cp\u003EPer i team aziendali, la questione pratica non è quale algoritmo scegliere, ma se l’organizzazione riesca a integrare dati governati, pipeline ripetibili, controlli del ciclo di vita dei modelli, valutazione e monitoraggio del ML in produzione in un unico modello operativo, mantenendoli connessi durante la scalabilità del sistema. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_a-practical-path-to-safer-ai_0":{"id":"text-cd85137839","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EIN SINTESI\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EIl machine learning in ambito enterprise richiede che l’organizzazione integri dati di alta qualità, pipeline ripetibili, governance e monitoraggio continuo in una base operativa unificata. 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