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En Administra proyectos de AA, verás cómo administrar un proyecto de AA a medida que avanza desde una idea hasta una implementación lista para la producción. En este curso, se abordan las fases de desarrollo del AA y las funciones y habilidades que suelen encontrarse en los equipos de AA. Se analizan estrategias para trabajar con las partes interesadas y se proporcionan detalles sobre cómo planificar y administrar un proyecto de AA en cada fase del desarrollo.
Mediante la desmitificación de las complejidades inherentes a los proyectos de AA, el curso proporciona un sólido framework teórico para administrar proyectos de AA.
El curso se enfoca en modelos de AA tradicionales. Aunque la IA generativa está en el centro de atención, el AA tradicional desempeña un papel fundamental en Google, ya que respalda muchos servicios y proyectos, desde la predicción de tiempos de viaje en Maps hasta la estimación del precio de los pasajes de avión en vuelos, desde la predicción de la cuota de TPU de Google Cloud hasta la recomendación de videos relevantes en YouTube.
En general, los principios para administrar proyectos de AA tradicionales son idénticos a los de la administración de proyectos de IA generativa. Cuando hay una diferencia significativa, el curso brinda
recomendaciones y orientación sobre IA generativa.
[null,null,["Última actualización: 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\n\n*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"]]
