Mantieni tutto organizzato con le raccolte Salva e classifica i contenuti in base alle tue preferenze.
Gestione dei progetti ML mostra come gestire un progetto ML man mano che passa da un'idea a un'implementazione pronta per la produzione. Il corso tratta le fasi di sviluppo del ML e i ruoli e le competenze tipici dei team ML. Discute le strategie per la collaborazione con gli stakeholder e fornisce dettagli su come pianificare e gestire un progetto ML in ogni fase di sviluppo.
Demistando le complessità inerenti ai progetti ML, il corso fornisce un solido framework teorico per la gestione dei progetti ML.
Il corso è incentrato sui modelli ML tradizionali. Sebbene l'IA generativa sia sotto i riflettori, il machine learning tradizionale svolge un ruolo vitale per Google, alla base di molti servizi e progetti, dalla previsione dei tempi di percorrenza in Maps alla stima del prezzo dei biglietti aerei in Voli, dalla previsione della quota TPU per Google Cloud al consiglio di video pertinenti su YouTube.
In generale, i principi per la gestione dei progetti ML tradizionali sono identici per la gestione dei progetti di IA generativa. Quando c'è una differenza significativa, il corso offre consigli e linee guida pertinenti per l'IA generativa.
Devi prima verificare che il machine learning sia l'approccio giusto per il tuo problema. Se non hai individuato il problema in termini di soluzione ML, consulta Introduction to Machine Learning Problem Framing.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Mancano le informazioni di cui ho bisogno","missingTheInformationINeed","thumb-down"],["Troppo complicato/troppi passaggi","tooComplicatedTooManySteps","thumb-down"],["Obsoleti","outOfDate","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Problema relativo a esempi/codice","samplesCodeIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2024-03-12 UTC."],[[["\u003cp\u003eThis course provides a comprehensive framework for managing machine learning (ML) projects, guiding you through all stages from ideation to production.\u003c/p\u003e\n"],["\u003cp\u003eIt covers key aspects such as defining project phases, planning and management strategies, establishing success metrics, and implementing responsible AI practices.\u003c/p\u003e\n"],["\u003cp\u003eWhile focused on traditional ML models, the course also offers insights into managing generative AI projects, highlighting common principles and key differences.\u003c/p\u003e\n"],["\u003cp\u003eTo benefit from this course, you should have a basic understanding of machine learning and have already determined that ML is the appropriate solution for your problem.\u003c/p\u003e\n"],["\u003cp\u003eIt's estimated to take approximately 90 minutes to complete this course, equipping you with the necessary skills to effectively manage your ML projects.\u003c/p\u003e\n"]]],[],null,["*Managing ML Projects* shows you how to manage an ML project as it progresses\nfrom an idea to a production-ready implementation. The course covers the\nML development phases and the roles and skills\ntypically found on ML teams. It discusses strategies for working with\nstakeholders and provides details on how to plan and manage an ML project\nat each phase of development.\n\nBy demystifying the complexities inherent in ML projects, the course\nprovides a solid theoretical framework for managing ML projects.\n\nThe course focuses on traditional ML models. Although generative AI is in the\nspotlight, traditional ML plays a vital role at Google, underpinning many\nservices and projects, from predicting travel times in Maps to estimating the\nprice of airline tickets in Flights, from predicting compute quota for Google\nCloud customers to recommending relevant videos in YouTube.\n\nIn general, the principles for managing traditional ML projects are identical\nfor managing generative AI projects. When there's a significant difference, the\ncourse provides relevant generative AI advice and guidance.\n| **Estimated Course Length:** 90 minutes\n| **Objectives:**\n|\n| - Define the phases and elements of an ML project.\n| - Describe how to plan and manage an ML project.\n| - Determine business and model success metrics.\n| - Recognize the iterative process of running ML experiments.\n| - Design a solution for productionizing ML pipelines.\n| - Implement responsible ML and AI practices at each development phase.\n\n**Prerequisites:**\n\n- You should have a basic understanding of machine learning. For a brief introduction to machine learning concepts, see [Introduction to Machine Learning](/machine-learning/intro-to-ml). For a hands-on introduction to machine learning, see [Machine\n Learning Crash Course](/machine-learning/crash-course).\n- You should first verify that ML is the right approach for your problem. If you haven't framed your problem in terms of an ML solution, complete [Introduction to Machine\n Learning Problem Framing](/machine-learning/problem-framing).\n\n\u003cbr /\u003e"]]
