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Google Cloud Training

En este curso, aprenderá de los ingenieros y capacitadores de AA que trabajan en el desarrollo de vanguardia de las canalizaciones de AA en Google Cloud. En los primeros módulos, se abordará TensorFlow Extended (o TFX), la plataforma de aprendizaje automático de producción de Google basada en TensorFlow para la administración de canalizaciones y metadatos de AA. Aprenderá sobre los componentes y la organización de las canalizaciones con TFX. También aprenderá cómo automatizar su canalización mediante la integración y la implementación continuas, y cómo administrar ML Metadata. Luego, cambiaremos el enfoque para analizar cómo podemos automatizar y volver a usar las canalizaciones de AA en múltiples frameworks de AA, como TensorFlow, PyTorch, scikit-learn y XGBoost. Además, aprenderá a usar Cloud Composer, otra herramienta de Google Cloud, para organizar sus canalizaciones de entrenamiento continuo. Por último, aprenderá a usar MLflow para administrar el ciclo de vida completo del aprendizaje automático.

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What's inside

Syllabus

Introducción
En este módulo, se describe la organización del curso
Introducción a las canalizaciones de TFX
En este módulo, se presenta TensorFlow Extended (o TFX) y se abordan los conceptos y componentes de TFX
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Organización de las canalizaciones con TFX
Esto es lo que abordaremos en este módulo:
Componentes personalizados y CI/CD para canalizaciones de TFX
ML Metadatos con TFX
Este módulo trata sobre el uso de metadatos de TFX para la administración de artefactos
Entrenamiento continuo con múltiples SDK, Kubeflow y AI Platform Pipelines
En este módulo, se aborda el entrenamiento continuo con múltiples SDK, Kubeflow y AI Platform Pipelines
Entrenamiento continuo con Cloud Composer
En este módulo, se aborda el entrenamiento continuo con Cloud Composer
Canalizaciones de AA con MLflow
En este módulo, se presenta MLflow y sus componentes
Resumen
En este módulo, se describe un resumen del curso

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Se enfoca en las canalizaciones de aprendizaje automático (ML) en Google Cloud
Impartido por ingenieros y capacitadores de AA que trabajan en el desarrollo de canalizaciones de AA de vanguardia en Google Cloud
Utiliza TensorFlow Extended (TFX), la plataforma de aprendizaje automático de producción de Google basada en TensorFlow
Aborda la automatización de canalizaciones mediante integración y implementación continuas
Enseña cómo administrar ML Metadata para el manejo de artefactos
Presenta Cloud Composer para organizar canalizaciones de entrenamiento continuo
Aborda el uso de MLflow para gestionar el ciclo de vida completo del aprendizaje automático
Requiere conocimientos previos en aprendizaje automático y tecnologías relacionadas
Es más adecuado para profesionales de aprendizaje automático, data scientists e ingenieros de datos que ya están familiarizados con las canalizaciones de AA

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in ML Pipelines on Google Cloud en Español with these activities:
Revisar los Módulos del Curso
Compilar y revisar los materiales del curso, como módulos y diapositivas, reforzará sus conocimientos y lo preparará para el éxito en el curso.
Show steps
  • Descargar y organizar los materiales del curso
  • Revisar los módulos y tomar notas
  • Identificar conceptos clave y términos
Tutoriales de TFX para Principiantes
Completar tutoriales guiados sobre TFX proporcionará experiencia práctica y fortalecerá su comprensión de los conceptos básicos de canalización de aprendizaje automático.
Show steps
  • Identificar tutoriales adecuados para su nivel de habilidad
  • Seguir los tutoriales paso a paso
  • Construir y ejecutar canalizaciones básicas de TFX
Sesiones de Estudio en Grupo
Participar en sesiones de estudio en grupo fomenta la colaboración, el intercambio de conocimientos y la comprensión más profunda de los conceptos del curso.
Show steps
  • Formar o unirse a un grupo de estudio
  • Establecer objetivos y un horario para las sesiones
  • Revisar el material del curso y discutir conceptos
  • Compartir ideas y perspectivas
Five other activities
Expand to see all activities and additional details
Show all eight activities
Ejercicios Prácticos de Construcción de Canalizaciones
Participar en ejercicios prácticos de construcción de canalizaciones reforzará su capacidad para diseñar e implementar canalizaciones de aprendizaje automático efectivas.
Show steps
  • Encontrar conjuntos de datos y problemas apropiados
  • Diseñar y construir canalizaciones utilizando TFX
  • Evaluar y optimizar el rendimiento de la canalización
Taller sobre Canalizaciones de Aprendizaje Automático Avanzadas
Asistir a un taller sobre canalizaciones de aprendizaje automático avanzadas le brindará exposición a técnicas y herramientas de vanguardia, ampliando su conjunto de habilidades y conocimiento.
Show steps
  • Investigar y seleccionar un taller relevante
  • Asistir al taller y participar activamente
  • Aplicar los conocimientos adquiridos a sus propios proyectos
Proyecto de Canalización de Aprendizaje Automático
Desarrollar un proyecto de canalización de aprendizaje automático le permitirá aplicar sus conocimientos y habilidades en un contexto del mundo real, mejorando su comprensión y retención.
Show steps
  • Definir el problema y los objetivos del proyecto
  • Recopilar y preparar datos
  • Diseñar y construir la canalización utilizando TFX
  • Implementar y monitorear la canalización
Contribuciones al Repositorio de Canalizaciones de TFX
Contribuir a un repositorio de canalizaciones de TFX de código abierto le permite colaborar con la comunidad, mejorar sus habilidades y mantenerse al día con las últimas tendencias.
Show steps
  • Identificar un área de contribución
  • Forkar el repositorio y realizar cambios
  • Enviar una solicitud de extracción
  • Revisar y recibir comentarios sobre su contribución
Mentoría de Estudiantes de Aprendizaje Automático
Servir como mentor para otros estudiantes de aprendizaje automático le permitirá reforzar su comprensión, desarrollar habilidades de liderazgo y contribuir a la comunidad de aprendizaje.
Show steps
  • Identificar plataformas o programas de tutoría
  • Ofrecer sus servicios como mentor
  • Proporcionar orientación y apoyo a los estudiantes

Career center

Learners who complete ML Pipelines on Google Cloud en Español will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their knowledge of AI algorithms, statistics, and programming to build ML models. As with Machine Learning Engineers, these models can be used to automate tasks and improve efficiency within almost any industry. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management.
Machine Learning Engineer
Machine Learning Engineers design and develop self-learning algorithms and ML models. These models and algorithms can be used to improve efficiency and automate tasks in virtually any domain or industry. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management.
Solutions Architect
Solutions Architects help businesses design and implement technology solutions to meet their needs. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Solutions Architects better understand and incorporate ML into their solutions.
Systems Engineer
Systems Engineers design, build, and maintain complex systems. These systems can include hardware, software, and networks. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Systems Engineers stay abreast of and even contribute to the development of new and emerging technologies and systems.
Database Administrator
Database Administrators maintain and manage databases. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Database Administrators stay abreast of new developments in the field and be better prepared to manage ML-related data.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Software Engineers stay up-to-date with the latest technologies and trends, and be better equipped to incorporate ML into their work.
IT Manager
IT Managers plan, implement, and manage IT systems. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help IT Managers stay up-to-date with the latest technologies and trends, and be better equipped to implement and manage ML systems.
Product Manager
Product Managers plan and develop products and services. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Product Managers better understand and use ML to develop innovative products and services.
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Data Analysts better understand and use ML to improve their work.
Operations Manager
Operations Managers plan and execute operations strategies. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Operations Managers better understand and use ML to improve their strategies and achieve operational excellence.
Business Analyst
Business Analysts help businesses define their needs and develop solutions. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Business Analysts better understand and use ML to improve business processes and outcomes.
Cybersecurity Analyst
Cybersecurity Analysts protect computer systems and networks from unauthorized access and attacks. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Cybersecurity Analysts better understand and use ML to improve their defenses and protect against cyber threats.
Sales Manager
Sales Managers plan and execute sales strategies. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Sales Managers better understand and use ML to improve their strategies and reach their sales goals.
Marketing Manager
Marketing Managers plan and execute marketing campaigns. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Marketing Managers better understand and use ML to improve their campaigns and reach their target audience.
Financial Analyst
Financial Analysts evaluate and recommend investments. This course may be useful for those who wish to pivot into this role as it helps build a foundation in ML fundamentals and advanced topics such as continuous training and metadata management. This background can help Financial Analysts better understand and use ML to improve their research and analysis.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in ML Pipelines on Google Cloud en Español.
Serves as a gentle introduction to the field of machine learning, using Python as the primary programming language. It covers essential concepts and algorithms in a clear and accessible manner.
Offers a hands-on approach to machine learning using popular libraries like Scikit-Learn, Keras, and TensorFlow. It provides practical knowledge and examples to help readers build and deploy ML models efficiently.
This comprehensive textbook provides an in-depth exploration of deep learning concepts, algorithms, and applications. It is considered a foundational work in the field, offering a detailed understanding of the underlying principles.
This classic textbook provides a solid foundation in statistical learning and data mining techniques. It covers a wide range of topics, including linear models, tree-based methods, and support vector machines.
This practical guide covers the fundamentals of Python for data analysis and manipulation. It is especially useful for beginners who want to build a strong foundation in Python for working with data.
This advanced textbook provides a probabilistic approach to machine learning, covering topics like Bayesian inference, graphical models, and reinforcement learning. It offers a deeper understanding of the theoretical foundations of ML.
Focuses on techniques for making machine learning models more interpretable and explainable. It valuable resource for understanding how to build and use models that are both accurate and transparent.
This concise guide provides a quick reference to essential concepts and best practices for building reliable data pipelines. It covers topics like data ingestion, transformation, and orchestration.

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