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Ma trasformare questi modelli in valore aziendale richiede più del semplice addestramento. 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border-top","layout":"SIMPLE","id":"container-103ecb5daf",":items":{"text":{"id":"text-f5fb6bdbfb","additionalClasses":"seo-hub-hero__related-topic-lable","text":"\u003Cp\u003EApprofondimenti sul deep learning:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_489983153":{"id":"text-3225c60d72","additionalClasses":"related-topics","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/deep-learning/computer-vision/\"\u003EVisione artificiale\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/deep-learning/neural-network/\"\u003EReti neurali\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text","text_489983153"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":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-bd178f8976","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",":items":{"callout__0":{"id":"text-242ebf61b9","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EDEEP LEARNING: LA DEFINIZIONE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EIl deep learning è un tipo di machine learning che utilizza reti neurali artificiali con numerosi livelli per apprendere schemi dai dati. Ogni livello trasforma in parte l’input, consentendo al sistema di apprendere caratteristiche sempre più complesse.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-f656bdb94e","text":"\u003Cp\u003EIl deep learning amplia le capacità del riconoscimento di schemi. I sistemi basati su regole e l’analisi dei dati tradizionale dipendono da input strutturati e logica esplicita, mentre i modelli di deep learning utilizzano livelli di neuroni artificiali per apprendere rappresentazioni dai dati. Questo li rende particolarmente adatti a immagini, linguaggio, voce, video e altri input complessi.\u003C/p\u003E\n\u003Cp\u003ERendere operativo il deep learning in un contesto aziendale è una disciplina diversa dallo sviluppo isolato di un modello. I modelli di deep learning richiedono in genere grandi set di dati, capacità di calcolo basata su GPU, una valutazione accurata e una governance rigorosa delle modalità di addestramento, controllo delle versioni, deployment e monitoraggio. I team necessitano di un’infrastruttura per sviluppare, addestrare, distribuire e governare i modelli su larga scala. Questa infrastruttura deve essere vicina ai dati e ai workflow per cui tali modelli sono creati.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-deep-learning":{"id":"title-v2-21fa622f0f","additionalClasses":"anchor-title anchor-title--what-is-deep-learning","type":"heading2","lines":["Che cos’è il deep learning?"],":type":"snowflake-site/components/title-v2"},"text_what-is-deep-learning_0":{"id":"text-e0bc370b4a","text":"\u003Cp\u003EIl deep learning è un sottoinsieme del machine learning che utilizza reti neurali multilivello per apprendere schemi dai dati. Una rete neurale è costituita da nodi connessi, o neuroni artificiali, organizzati in livelli. Ogni livello trasforma i dati ricevuti e trasmette una rappresentazione al livello successivo, consentendo al modello di apprendere schemi sempre più astratti man mano che le informazioni attraversano la rete.\u003C/p\u003E\n\u003Cp\u003EIn un modello per immagini, i primi livelli possono apprendere bordi e texture, mentre quelli successivi apprendono forme o oggetti. In un modello linguistico, i livelli possono apprendere le relazioni tra token, struttura delle frasi e contesto. In un modello addestrato su dati comportamentali o transazionali, i livelli possono apprendere combinazioni di segnali difficili da definire manualmente, come tempistiche, comportamento dei dispositivi e attività storiche.\u003C/p\u003E\n\u003Cp\u003EIl termine “deep” si riferisce al numero di livelli della rete, non a una capacità di ragionamento simile a quella umana. Un modello di deep learning non comprende un’immagine medica, un comando vocale o un messaggio di un cliente come farebbe una persona. Apprende relazioni statistiche dai dati di addestramento, quindi le utilizza per formulare previsioni, classificazioni o output generati.\u003C/p\u003E\n\u003Cp\u003EPer un’introduzione ai concetti fondamentali, consulta la nostra guida al \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E e le risorse correlate sull’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EAI generativa\u003C/a\u003E e sugli \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EScopri come la startup di cybersecurity DeepTempo utilizza il deep learning per contrastare le minacce alla sicurezza basate sull’AI:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-deep-learning_0":{"id":"embed-ea4a73070a","youtubeVideoId":"r3TL5aMUNHs","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_deep-learning-examples-and-use-cases":{"id":"title-v2-11bd0991d1","additionalClasses":"anchor-title anchor-title--deep-learning-examples-and-use-cases","type":"heading2","lines":["Esempi e casi d’uso del deep learning"],":type":"snowflake-site/components/title-v2"},"text_deep-learning-examples-and-use-cases_0":{"id":"text-8d0115e819","text":"\u003Cp\u003EIl deep learning è particolarmente utile quando i segnali da individuare sono contenuti in dati complessi, soprattutto se sarebbe difficile modellare tali dati usando soltanto caratteristiche definite manualmente.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ERilevamento delle frodi:\u003C/strong\u003E i team dei servizi finanziari possono utilizzare il deep learning per identificare schemi di transazioni sospette nel tempo, analizzando il comportamento dei conti, i dati dei dispositivi e l’attività degli esercenti. Un modello può apprendere sottili combinazioni di segnali difficili da rilevare con regole statiche, anche se i team necessitano comunque di spiegabilità, workflow di revisione e controlli per le decisioni ad alto rischio.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EManutenzione predittiva:\u003C/strong\u003E le aziende manifatturiere, le società di servizi di pubblica utilità e le aziende di trasporto possono addestrare modelli usando letture dei sensori, schemi di vibrazione, variazioni di temperatura e dati storici sulla manutenzione. Un modello di deep learning può apprendere segnali di allerta precoce che consentono ai team di prevedere i guasti delle apparecchiature prima che si verifichino un’interruzione visibile o un problema operativo.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ESistemi di raccomandazione:\u003C/strong\u003E le aziende del retail e dei media e le piattaforme digitali usano il deep learning per modellare le relazioni tra utenti, prodotti, contenuti e comportamenti. Questi sistemi possono migliorare la personalizzazione apprendendo da clic, acquisti, cronologie di visualizzazione, attributi dei prodotti e segnali contestuali.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDiagnostica per immagini:\u003C/strong\u003E le organizzazioni del settore Healthcare e Life Sciences usano il deep learning per task basati sulle immagini, come l’identificazione di pattern in scansioni radiologiche, vetrini istologici o immagini oftalmologiche. Questi modelli possono supportare i workflow clinici, ma richiedono un’attenta convalida, supervisione umana e governance, perché i loro output possono influire sull’assistenza ai pazienti.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EElaborazione del linguaggio naturale e chatbot:\u003C/strong\u003E i modelli di deep learning possono classificare testi, estrarre entità, riassumere documenti, tradurre lingue e supportare assistenti conversazionali. I modelli transformer sono particolarmente importanti in questo ambito perché usano meccanismi di attenzione per apprendere le relazioni all’interno delle sequenze, aiutando i modelli a cogliere il contesto nel linguaggio, nel codice, nell’audio e negli input multimodali.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EVeicoli autonomi e robotica:\u003C/strong\u003E i modelli di deep learning consentono ai sistemi di interpretare i dati dei sensori, rilevare oggetti, stimare il movimento e pianificare azioni. Questi casi d’uso combinano in genere computer vision, reinforcement learning, simulazione e controlli di sicurezza.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERiconoscimento vocale:\u003C/strong\u003E il deep learning può convertire il linguaggio parlato in testo, identificare chi parla, rilevare comandi o analizzare pattern audio. Questi sistemi combinano spesso modellazione acustica, modellazione linguistica ed elaborazione di sequenze.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_deep-learning-examples-and-use-cases_0":{"id":"text-aee5d46388","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EIN SINTESI\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EIl deep learning è particolarmente utile quando un problema dipende dall’identificazione di pattern in dati complessi, come immagini, linguaggio, audio, video, letture dei sensori o entità connesse.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-does-deep-learning-work":{"id":"title-v2-9d691f33fb","additionalClasses":"anchor-title anchor-title--how-does-deep-learning-work","type":"heading2","lines":["Come funziona il deep learning?"],":type":"snowflake-site/components/title-v2"},"text_how-does-deep-learning-work_0":{"id":"text-365da953cb","text":"\u003Cp\u003EUn modello di deep learning apprende confrontando ripetutamente i propri output con il risultato atteso, misurando la differenza e regolando i parametri interni per ridurre gli errori futuri.\u003C/p\u003E\n\u003Cp\u003EIl processo inizia con i dati di input. Per un modello di immagini, l’input può essere costituito dai valori dei pixel. Per un modello linguistico, l’input può essere costituito da token. Per un modello di serie temporali, l’input può essere una sequenza di letture dei sensori o eventi di transazione. Il modello trasmette l’input attraverso una serie di livelli, ognuno dei quali applica trasformazioni matematiche usando parametri appresi chiamati pesi.\u003C/p\u003E\n\u003Cp\u003EIl livello di output produce una previsione, una classificazione o un risultato generato. Una funzione di perdita misura quindi la distanza tra l’output e la risposta desiderata. In un modello di classificazione, la perdita può riflettere il grado di sicurezza con cui il modello ha previsto la categoria errata. In un modello di previsione, può misurare la differenza tra il valore previsto e quello effettivo.\u003C/p\u003E\n\u003Cp\u003ELa retropropagazione è il metodo di addestramento che trasmette il segnale di errore all’indietro attraverso la rete. Il modello usa questo segnale per regolare i pesi nei livelli precedenti, in genere mediante un’ottimizzazione basata sul gradiente. Nel corso di numerose iterazioni di addestramento, il modello aggiorna i propri parametri per ridurre la perdita sugli esempi di addestramento e, idealmente, ottenere buoni risultati sui nuovi dati.\u003C/p\u003E\n\u003Cp\u003EIn ambito enterprise, l’aspetto fondamentale è che l’addestramento non è solo un’attività di modellazione. I team necessitano di dati preparati, esperimenti ripetibili, metriche di valutazione, risorse di calcolo, controllo delle versioni, percorsi di deployment e monitoraggio. Un modello di deep learning può ottenere buoni risultati in un notebook ma non funzionare in produzione se i dati di input cambiano, i costi di inferenza sono troppo elevati o il modello non può essere governato in conformità ai requisiti dell’organizzazione in materia di rischio.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_types-of-deep-learning-models":{"id":"title-v2-abb6d4cc51","additionalClasses":"anchor-title anchor-title--types-of-deep-learning-models","type":"heading2","lines":["Tipi di modelli di deep learning"],":type":"snowflake-site/components/title-v2"},"text_types-of-deep-learning-models_0":{"id":"text-e91382f229","text":"\u003Cp\u003EIl deep learning non corrisponde a un solo tipo di modello. Le diverse architetture gestiscono strutture differenti nei dati: pixel in un’immagine, token in una frase, eventi in una sequenza, nodi in un grafo o segnali distribuiti tra più modalità. La scelta del modello giusto parte in genere dalla natura del problema, dal tipo di dati disponibili e dal tipo di output che il sistema deve produrre.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ETipo\u003C/th\u003E\u003Cth\u003EIdeale per\u003C/th\u003E\u003Cth\u003EEsempi\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\n\u003Ctbody\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EReti neurali feedforward\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EPrevisione e classificazione di base su dati strutturati\u003C/td\u003E\u003Ctd\u003EPercettroni multistrato (MLP)\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EReti neurali convoluzionali\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EImmagini, video e pattern spaziali\u003C/td\u003E\u003Ctd\u003EResNet, VGG, EfficientNet\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EReti neurali ricorrenti\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ESequenze e dati di serie temporali\u003C/td\u003E\u003Ctd\u003ERNN, LSTM, GRU\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ETransformer\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ELinguaggio, visione, audio e task multimodali\u003C/td\u003E\u003Ctd\u003EGPT, BERT, T5, Vision Transformer\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EAutoencoder\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ECompressione, denoising e rilevamento delle anomalie\u003C/td\u003E\u003Ctd\u003EAutoencoder variazionali, autoencoder di denoising\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EReti generative avversarie\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EGenerazione di immagini, audio e dati realistici\u003C/td\u003E\u003Ctd\u003EGAN, StyleGAN, CycleGAN\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EModelli di diffusione\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EGenerazione di immagini, audio e video di alta qualità\u003C/td\u003E\u003Ctd\u003EDDPM, modelli in stile Stable Diffusion\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EReti neurali a grafo\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EDati rappresentati come nodi e archi\u003C/td\u003E\u003Ctd\u003ESocial network, molecole, grafi di raccomandazione\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EModelli di deep reinforcement learning\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EApprendimento delle azioni tramite ricompense\u003C/td\u003E\u003Ctd\u003EDQN, sistemi in stile AlphaGo\u003C/td\u003E\u003C/tr\u003E\n\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EModelli multimodali\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ECombinazione di testo, immagini, audio e video\u003C/td\u003E\u003Ctd\u003EModelli visione-linguaggio, sistemi di generazione di didascalie per immagini\u003C/td\u003E\u003C/tr\u003E\n\u003C/tbody\u003E\u003C/table\u003E\n\u003Ch3\u003EReti neurali feedforward\u003C/h3\u003E\n\u003Cp\u003ELe reti neurali feedforward trasferiscono i dati in un’unica direzione, dal livello di input ai livelli nascosti fino al livello di output. Sono spesso utilizzate per task di previsione strutturata e classificazione in cui le feature di input sono già ben definite, come prevedere il churn, classificare il rischio o assegnare un punteggio ai lead.\u003C/p\u003E\n\u003Ch3\u003EReti neurali convoluzionali\u003C/h3\u003E\n\u003Cp\u003ELe reti neurali convoluzionali, spesso chiamate CNN, rilevano pattern spaziali. Sono comunemente utilizzate per immagini e video perché apprendono pattern locali come bordi, texture e forme, quindi combinano questi segnali in rappresentazioni di livello superiore.\u003C/p\u003E\n\u003Ch3\u003EReti neurali ricorrenti\u003C/h3\u003E\n\u003Cp\u003ELe reti neurali ricorrenti elaborano le sequenze trasferendo le informazioni dai passaggi precedenti a quelli successivi. Le reti con memoria a lungo e breve termine e le unità ricorrenti con gate sono state ampiamente utilizzate per task linguistici e relativi alle serie temporali perché modellano l’ordine e le dipendenze temporali, sebbene i transformer le abbiano sostituite in molti sistemi linguistici e multimodali.\u003C/p\u003E\n\u003Ch3\u003ETransformer\u003C/h3\u003E\n\u003Cp\u003EI transformer utilizzano meccanismi di attenzione per modellare le relazioni all’interno di una sequenza. Anziché elaborare i token rigorosamente uno alla volta, apprendono quali parti dell’input devono influenzarsi a vicenda. Questa architettura è alla base di molti modelli linguistici moderni, modelli visione-linguaggio e altri foundation model.\u003C/p\u003E\n\u003Ch3\u003EAutoencoder\u003C/h3\u003E\n\u003Cp\u003EGli autoencoder apprendono rappresentazioni compresse dei dati di input, quindi ricostruiscono l’input originale a partire dalla forma compressa. Sono utili per la riduzione della dimensionalità, il denoising e il rilevamento delle anomalie, perché gli errori di ricostruzione possono evidenziare punti dati che si discostano dai pattern normali.\u003C/p\u003E\n\u003Ch3\u003EReti generative avversarie\u003C/h3\u003E\n\u003Cp\u003ELe reti generative avversarie, o GAN, utilizzano due reti: un generatore che crea output sintetici e un discriminatore che valuta se tali output assomigliano a dati reali. Le GAN sono state utilizzate per la generazione di immagini, lo style transfer e la generazione di dati sintetici, sebbene possa essere difficile addestrarle in modo affidabile.\u003C/p\u003E\n\u003Ch3\u003EModelli di diffusione\u003C/h3\u003E\n\u003Cp\u003EI modelli di diffusione generano output apprendendo come invertire un processo che aggiunge rumore. Sono ampiamente associati alla generazione di immagini di alta qualità e vengono utilizzati anche per audio, video e altri task generativi. Nei contesti aziendali, il loro valore dipende dal caso d’uso, dai diritti sui dati, dai requisiti di latenza e dai controlli di governance dei contenuti.\u003C/p\u003E\n\u003Ch3\u003EReti neurali a grafo\u003C/h3\u003E\n\u003Cp\u003ELe reti neurali a grafo modellano i dati come nodi e archi. Sono utili quando le relazioni tra le entità sono importanti quanto le entità stesse, come clienti e prodotti in un grafo di raccomandazione, conti e transazioni in una rete per il rilevamento delle frodi oppure molecole e legami nella ricerca per la scoperta di farmaci.\u003C/p\u003E\n\u003Ch3\u003EModelli di deep reinforcement learning\u003C/h3\u003E\n\u003Cp\u003EIl deep reinforcement learning combina le reti neurali con l’apprendimento basato sulle ricompense. Anziché apprendere solo da esempi etichettati, un agente impara eseguendo azioni, ricevendo ricompense o penalità e adattando il proprio comportamento nel tempo. Questi modelli sono utilizzati nella robotica, nei giochi, nell’ottimizzazione e in alcuni sistemi decisionali autonomi.\u003C/p\u003E\n\u003Ch3\u003EModelli multimodali\u003C/h3\u003E\n\u003Cp\u003EI modelli multimodali elaborano più tipi di dati, come testo e immagini, audio e video oppure record strutturati e documenti non strutturati. Ad esempio, un modello multimodale per l’assistenza clienti potrebbe analizzare insieme l’immagine di un prodotto, un ticket di assistenza, la cronologia degli acquisti e la documentazione per la risoluzione dei problemi, anziché trattare ciascun input come un sistema separato.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ml-vs-deep-learning-vs-generative-ai":{"id":"title-v2-6f262695e3","additionalClasses":"anchor-title anchor-title--ml-vs-deep-learning-vs-generative-ai","type":"heading2","lines":["ML, deep learning e AI generativa a confronto"],":type":"snowflake-site/components/title-v2"},"text_ml-vs-deep-learning-vs-generative-ai_0":{"id":"text-9191ff6f7f","text":"\u003Cp\u003EMachine learning, deep learning e AI generativa sono discipline correlate, ma hanno ambiti diversi. Il ML è la disciplina più ampia. Il deep learning è un sottoinsieme del ML e l’AI generativa è un sottoinsieme del deep learning.\u003C/p\u003E\n\u003Cp\u003EIl ML comprende qualsiasi sistema che apprende pattern dai dati anziché seguire regole programmate esplicitamente. Il deep learning utilizza reti neurali multistrato, particolarmente utili quando gli input sono così complessi o non strutturati che i modelli più semplici faticano a individuare il segnale rilevante. Il riconoscimento delle immagini, l’elaborazione del parlato e la comprensione dei documenti sono usi comuni, anche se il deep learning può elaborare dati strutturati quando il problema lo richiede.\u003C/p\u003E\n\u003Cp\u003EL’AI generativa comprende sistemi che producono nuovi contenuti, come testo, immagini, codice e audio, anziché eseguire classificazioni o previsioni. La maggior parte dei sistemi di AI generativa si basa su architetture transformer o di diffusione, ma la categoria non comprende l’intero ambito del deep learning.\u003C/p\u003E\n\u003Cp\u003ELa maggior parte dei \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Esistemi AI\u003C/a\u003E aziendali combina diversi approcci anziché rientrare nettamente in un unico paradigma. Ad esempio, un modello di base addestrato su esempi etichettati può essere adattato a un task correlato tramite transfer learning e successivamente perfezionato attraverso il feedback umano. Quando i dati etichettati sono scarsi, i metodi non supervisionati possono far emergere strutture che il modello altrimenti non rileverebbe.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_ml-vs-deep-learning-vs-generative-ai_0":{"id":"text-c02cd697be","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EL’ERRORE COMUNE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EIl deep learning non deve essere considerato la scelta di default per i problemi di AI. Se un modello ML più semplice produce risultati affidabili con costi inferiori, spiegazioni più chiare e una minore complessità operativa, il deep learning può comportare più oneri che vantaggi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_advantages-and-disadvantages-of-deep-learning":{"id":"title-v2-dc09b2c2e8","additionalClasses":"anchor-title anchor-title--advantages-and-disadvantages-of-deep-learning","type":"heading2","lines":["Vantaggi e svantaggi del deep learning"],":type":"snowflake-site/components/title-v2"},"text_advantages-and-disadvantages-of-deep-learning_0":{"id":"text-15664c31d0","text":"\u003Cp\u003EIl deep learning è utilizzato in ambito aziendale perché apprende pattern complessi da grandi quantità di dati. È utile per i task che coinvolgono immagini, parlato, linguaggio, video, raccomandazioni, stream di sensori e altri tipi di dati nei quali il segnale rilevante potrebbe non essere evidente a chi definisce le regole o seleziona le feature.\u003C/p\u003E\r\n\u003Cp\u003EPuò inoltre ridurre la necessità di eseguire manualmente la feature engineering. In molti workflow ML tradizionali, i data scientist dedicano molto tempo a decidere quali feature devono rappresentare il problema. I modelli di deep learning possono apprendere rappresentazioni utili direttamente dai dati, anche se i team devono comunque adottare solide pratiche di preparazione, etichettatura, valutazione e monitoraggio dei dati.\u003C/p\u003E\r\n\u003Cp\u003ELe prestazioni del deep learning possono migliorare con l’aumento dei dati e della capacità di calcolo disponibili, soprattutto quando il problema trae vantaggio dalla scalabilità. Supporta inoltre il transfer learning, che consente ai team di adattare a un task correlato un modello addestrato su un data set di grandi dimensioni. Questo approccio può ridurre la quantità necessaria di dati specifici per il task e abbreviare il percorso dalla sperimentazione al raggiungimento di prestazioni utili.\u003C/p\u003E\r\n\u003Cp\u003ETuttavia, i compromessi sono rilevanti. I modelli di deep learning richiedono spesso grandi volumi di dati di alta qualità e l’addestramento può essere costoso perché potrebbe richiedere GPU o altre risorse di elaborazione specializzate. Questi modelli possono anche essere difficili da interpretare, soprattutto quando contengono molti livelli e parametri. Ciò crea difficoltà negli ambiti in cui i team devono spiegare, sottoporre ad audit o giustificare il comportamento del modello.\u003C/p\u003E\r\n\u003Cp\u003EI modelli di deep learning possono anche incorrere nell’overfitting. Un modello può ottenere buoni risultati sui dati di addestramento, ma non riuscire a elaborarare correttamente nuovi esempi se ha appreso rumore, scorciatoie o pattern che non si generalizzano. Anche i bias nei dati di addestramento possono influire sul comportamento del modello, soprattutto quando viene utilizzato in contesti ad alto impatto come la concessione di prestiti, le assunzioni, il settore sanitario o i servizi pubblici. In Italia, AgID pone l’attenzione su un’\u003Ca href=\"https://www.agid.gov.it/it/ambiti-intervento/intelligenza-artificiale\" target=\"_blank\"\u003Eadozione responsabile dell’IA nella Pubblica Amministrazione\u003C/a\u003E, che tenga conto anche dei rischi di discriminazione e della necessità di garantire equità, ad esempio nell’accesso all’occupazione e ai servizi pubblici o nel trattamento di persone appartenenti a gruppi diversi.\u003C/p\u003E\r\n\u003Cp\u003EIl deep learning è particolarmente efficace quando il problema implica l’analisi di pattern complessi, l’organizzazione dispone di una quantità sufficiente di dati pertinenti e i team sono in grado di soddisfare i requisiti in termini di capacità di calcolo, valutazione e governance. Tuttavia, potrebbe non essere adatto con data set di piccoli dimensioni, se il problema aziendale è semplice, il budget è limitato o una spiegazione chiara conta più di miglioramenti marginali dell’accuratezza.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_deep-learning-on-snowflake":{"id":"title-v2-47286b47ba","additionalClasses":"anchor-title anchor-title--deep-learning-on-snowflake","type":"heading2","lines":["Deep learning su Snowflake"],":type":"snowflake-site/components/title-v2"},"text_deep-learning-on-snowflake_0":{"id":"text-eea7e075ba","text":"\u003Cp\u003EI team che utilizzano il deep learning devono accedere a dati governati, preparare le feature, eseguire esperimenti, addestrare i modelli, monitorarne le versioni, distribuirli per l’inferenza e controllarne le prestazioni nel tempo. Snowflake supporta questi workflow tramite \u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003ESnowflake ML offre funzionalità per preparare i dati, creare e utilizzare feature, addestrare modelli con CPU o GPU, valutare i modelli, rendere operative le pipeline, distribuire i modelli e monitorare quelli in produzione.\u003C/p\u003E\n\u003Cp\u003EContainer Runtime for ML offre  ambienti preconfigurati e personalizzabili per il ML su Snowpark Container Services. Snowflake Notebooks offre un ambiente di sviluppo interattivo in cui i team possono lavorare con dati, codice e workflow ML. Utilizzati con Container Runtime, i notebook supportano workload avanzati di data science e ML tramite opzioni software e hardware adatte alle esigenze più complesse di addestramento e sperimentazione.\u003C/p\u003E\n\u003Cp\u003ESnowflake Model Registry consente di gestire modelli e metadati in Snowflake, indipendentemente dall’origine e dal tipo. Dopo l’addestramento, i team possono registrare un modello nel registro, gestirne le versioni ed eseguire l’inferenza in Snowflake. La documentazione Snowflake descrive il registro come uno strumento per gestire in modo sicuro i modelli e i relativi metadati, semplificando il percorso dallo sviluppo alla produzione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_deep-learning-in-enterprise-environments":{"id":"title-v2-da1cbc65c8","additionalClasses":"anchor-title anchor-title--deep-learning-in-enterprise-environments","type":"heading2","lines":["Deep learning negli ambienti aziendali"],":type":"snowflake-site/components/title-v2"},"text_deep-learning-in-enterprise-environments_0":{"id":"text-42ed6148f1","text":"\u003Cp\u003EIl deep learning consente alle organizzazioni di utilizzare dati che in precedenza erano troppo complessi per essere modellizzati in modo efficace, ma il suo valore dipende dai controlli che rendono i modelli utilizzabili, tracciabili e affidabili in produzione. I team devono sapere quali dati hanno contribuito a plasmare il modello, come è stato valutato, dove è distribuito, che cosa è cambiato dall’addestramento e chi ne risponde quando l’output influisce su un workflow.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_deep-learning-in-enterprise-environments_0":{"id":"text-a3ad696bab","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EIN SINTESI\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EIl deep learning consente alle organizzazioni di individuare nei dati complessi pattern che gli approcci tradizionali spesso non rilevano. Ma generare valore aziendale richiede più dell’addestramento di un modello: dipende dai dati, dall’infrastruttura, dalla governance e dai processi operativi necessari per distribuire e gestire l’AI su vasta scala.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-deep-learning","text_what-is-deep-learning_0","yt_what-is-deep-learning_0","title_deep-learning-examples-and-use-cases","text_deep-learning-examples-and-use-cases_0","callout_deep-learning-examples-and-use-cases_0","title_how-does-deep-learning-work","text_how-does-deep-learning-work_0","title_types-of-deep-learning-models","text_types-of-deep-learning-models_0","title_ml-vs-deep-learning-vs-generative-ai","text_ml-vs-deep-learning-vs-generative-ai_0","callout_ml-vs-deep-learning-vs-generative-ai_0","title_advantages-and-disadvantages-of-deep-learning","text_advantages-and-disadvantages-of-deep-learning_0","title_deep-learning-on-snowflake","text_deep-learning-on-snowflake_0","title_deep-learning-in-enterprise-environments","text_deep-learning-in-enterprise-environments_0","callout_deep-learning-in-enterprise-environments_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid 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