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Google Cloud Training
En este curso, analizaremos los componentes y las prácticas recomendadas de la creación de sistemas de AA de alto rendimiento en entornos de producción. Veremos algunas de las consideraciones más comunes tras la creación de estos sistemas, p. ej., entrenamiento estático, entrenamiento dinámico, inferencia estática, inferencia dinámica, TensorFlow distribuido y TPU. Este curso se enfoca en explorar las características que conforman un buen sistema de AA más allá de su capacidad de realizar predicciones correctas.
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What's inside
Syllabus
Introducción al aprendizaje automático avanzado en Google Cloud
En este módulo, se verá un avance de los temas que se abordan en el curso y cómo usar Qwiklabs para completar cada uno de tus labs con Google Cloud.
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Syllabus
Introducción al aprendizaje automático avanzado en Google Cloud
En este módulo, se verá un avance de los temas que se abordan en el curso y cómo usar Qwiklabs para completar cada uno de tus labs con Google Cloud.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Especializado en el diseño de sistemas de aprendizaje automático de alto rendimiento y adaptables
Impartido por Google Cloud Training, expertos reconocidos en el campo del aprendizaje automático
Cubre temas esenciales para la implementación de sistemas de aprendizaje automático en producción
Incluye laboratorios prácticos para reforzar los conceptos y habilidades aprendidas
Dirigido a ingenieros, científicos de datos y profesionales de aprendizaje automático que buscan mejorar sus habilidades en el diseño e implementación de sistemas de aprendizaje automático robustos
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Reviews summary
Sistemas de ml en producción con google cloud
Según los estudiantes, este curso de Sistemas de Machine Learning en Producción de Google Cloud es esencial para aplicar el ML en entornos reales. Aborda con profundidad la arquitectura y diseño de sistemas de alto rendimiento. Los módulos están bien estructurados y ofrecen una visión clara de cómo manejar el entrenamiento y la inferencia de modelos. Los ejercicios prácticos con Qwiklabs son clave para entender la implementación con TensorFlow distribuido y TPU. Algunos mencionan que el curso requiere una base sólida en ML y programación. Es una excelente guía para profesionales que buscan llevar sus conocimientos de ML a la producción.
El ritmo del ML exige revisiones periódicas para mantener el contenido relevante.
"Dada la rapidez con la que avanza el Machine Learning, sería bueno que el curso se actualizara regularmente."
"Aunque el contenido es excelente, el campo de ML en producción cambia muy rápido, lo que podría afectar su relevancia a largo plazo."
"Esperaría que Google Cloud mantuviera este curso al día con las últimas prácticas y herramientas de MLOps."
Explora la arquitectura y consideraciones de diseño para sistemas ML robustos.
"El curso me ayudó a entender mejor las decisiones de diseño para entrenamiento y entrega de modelos en producción."
"Me sorprendió la claridad con la que se explican las consideraciones para sistemas adaptables y de alto rendimiento."
"Aborda no solo cómo predecir, sino cómo construir un sistema de ML que realmente funcione bien en un entorno real."
Aporta conocimientos aplicables directamente en entornos de producción con Google Cloud.
"Aprendí a diseñar sistemas de ML de alto rendimiento usando las herramientas de Google Cloud, lo que es muy útil en mi trabajo."
"Los labs de Qwiklabs me permitieron experimentar de primera mano con TensorFlow distribuido y TPUs, muy valioso."
"Este curso me dio una visión clara de cómo llevar mis modelos de ML a producción, más allá de la teoría."
Demanda una base sólida en aprendizaje automático y programación.
"Siento que este curso es para personas que ya tienen experiencia en ML, no para principiantes."
"Aunque muy completo, creo que se beneficiaría de un recordatorio de conceptos básicos de Machine Learning antes de empezar."
"Para aprovecharlo al máximo, es clave tener conocimientos previos de Python y de los fundamentos de Machine Learning."
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 Production Machine Learning Systems - Español with these
activities:
Revisión y organización de materiales del curso
Show steps
Organiza y revisa los materiales del curso para mejorar la retención y la comprensión.
Show steps
-
Recopila y organiza notas, tareas, cuestionarios y exámenes
-
Resalta y resume los conceptos clave
Contribuir a proyectos de AA de código abierto
Show steps
Contribuye a la comunidad de AA participando en proyectos de código abierto para ampliar tu experiencia y conocimientos.
Show steps
-
Identifica proyectos de AA de código abierto que te interesen
-
Examina el código, identifica errores y sugiere mejoras
-
Colabora con otros desarrolladores para implementar cambios
Show all two activities
Revisión y organización de materiales del curso
Show steps
Organiza y revisa los materiales del curso para mejorar la retención y la comprensión.
Show steps
Show steps
Show steps
- Recopila y organiza notas, tareas, cuestionarios y exámenes
- Resalta y resume los conceptos clave
Contribuir a proyectos de AA de código abierto
Show steps
Contribuye a la comunidad de AA participando en proyectos de código abierto para ampliar tu experiencia y conocimientos.
Show steps
Show steps
Show steps
- Identifica proyectos de AA de código abierto que te interesen
- Examina el código, identifica errores y sugiere mejoras
- Colabora con otros desarrolladores para implementar cambios
Show all two activities
Career center
Learners who complete Production Machine Learning Systems - Español will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course provides a comprehensive overview of the machine learning lifecycle, from data collection to model deployment. The course covers topics such as model selection, hyperparameter tuning, and model evaluation, which are all essential for building and deploying successful machine learning models. Additionally, the course provides hands-on experience with Google Cloud Platform, which is one of the leading platforms for developing and deploying machine learning models.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in the principles and practices of machine learning, which is a key skill for Data Analysts. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models. Additionally, the course provides hands-on experience with Google Cloud Platform, which is one of the leading platforms for developing and deploying machine learning models.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in the principles and practices of machine learning, which is a key skill for Data Scientists. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models. Additionally, the course provides hands-on experience with Google Cloud Platform, which is one of the leading platforms for developing and deploying machine learning models.
Product Manager
Product Manager
Product Managers are responsible for defining and managing the development of products. This course may be useful for Product Managers who want to learn more about machine learning and how it can be used to improve products. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course may be useful for Marketing Managers who want to learn more about machine learning and how it can be used to improve marketing campaigns. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. This course may be useful for Sales Managers who want to learn more about machine learning and how it can be used to improve sales performance. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with a company's products or services. This course may be useful for Customer Success Managers who want to learn more about machine learning and how it can be used to improve customer satisfaction. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Operations Manager
Operations Manager
Operations Managers are responsible for overseeing the day-to-day operations of a business. This course may be useful for Operations Managers who want to learn more about machine learning and how it can be used to improve operational efficiency. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making recommendations to investors. This course may be useful for Financial Analysts who want to learn more about machine learning and how it can be used to improve investment performance. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and mitigating risks to a business. This course may be useful for Risk Managers who want to learn more about machine learning and how it can be used to improve risk management. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Compliance Officer
Compliance Officer
Compliance Officers are responsible for ensuring that a business complies with all applicable laws and regulations. This course may be useful for Compliance Officers who want to learn more about machine learning and how it can be used to improve compliance. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Information Security Analyst
Information Security Analyst
Information Security Analysts are responsible for protecting a business's information systems from unauthorized access or use. This course may be useful for Information Security Analysts who want to learn more about machine learning and how it can be used to improve information security. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts are responsible for ensuring that data is used in a consistent and ethical manner. This course may be useful for Data Governance Analysts who want to learn more about machine learning and how it can be used to improve data governance. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course may be useful for Software Engineers who want to learn more about machine learning and how to apply it to their work. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models. Additionally, the course provides hands-on experience with Google Cloud Platform, which is one of the leading platforms for developing and deploying machine learning models.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business needs and recommending solutions to improve efficiency and profitability. This course may be useful for Business Analysts who want to learn more about machine learning and how it can be used to improve business outcomes. The course covers topics such as data preprocessing, model training, and model evaluation, which are all essential for building and deploying successful machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/y6ba3i/production
Reading list
We've selected nine 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
Production Machine Learning Systems - Español.
Save
Provides a practical guide to using R for data science, covering topics such as data cleaning, manipulation, and visualization.
Provides a practical guide to using Python for data analysis, covering topics such as data cleaning, manipulation, and visualization.
Covers the fundamentals of machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Covers the fundamentals of deep learning, including topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a business-oriented overview of data science, covering topics such as data collection, analysis, and visualization.
Covers the principles and patterns for designing and building data-intensive applications, including topics such as data storage, processing, and management.
Save
Provides a broad overview of big data, covering topics such as data storage, processing, and analysis.
Provides a non-technical introduction to data analytics, covering topics such as data collection, analysis, and visualization.
Save
Este libro proporciona una introducción completa al aprendizaje profundo y sus aplicaciones. Es un recurso útil para aquellos que buscan profundizar sus conocimientos sobre los modelos de redes neuronales y sus técnicas de entrenamiento.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y6ba3i/production
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Effort
5 - 7 hours per week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Español
Transcript
English
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