In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
Este curso forma parte de Programa especializado: Machine Learning Engineering for Production (MLOps)
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Acerca de este Curso
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Fechas lÃmite flexibles
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Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel avanzado
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 26 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English), Francés (French)
Qué aprenderás
Apply techniques to manage modeling resources and best serve batch and real-time inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
Habilidades que obtendrás
- Explainable AI
- Fairness Indicators
- automl
- Model Performance Analysis
- Precomputing Predictions
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel avanzado
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 26 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English), Francés (French)
ofrecido por
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - Qué aprenderás en este curso
7 horas para completar
Week 1: Neural Architecture Search
Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.
7 horas para completar
10 videos (Total 40 minutos), 4 lecturas, 6 cuestionarios
5 horas para completar
Week 2: Model Resource Management Techniques
Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.
5 horas para completar
13 videos (Total 91 minutos), 6 lecturas, 3 cuestionarios
5 horas para completar
Week 3: High-Performance Modeling
Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.
5 horas para completar
6 videos (Total 57 minutos), 4 lecturas, 4 cuestionarios
6 horas para completar
Week 4: Model Analysis
Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.
6 horas para completar
12 videos (Total 69 minutos), 8 lecturas, 5 cuestionarios
Reseñas
- 5 stars66,91Â %
- 4 stars19,85Â %
- 3 stars6,25Â %
- 2 stars4,41Â %
- 1 star2,57Â %
Principales reseñas sobre MACHINE LEARNING MODELING PIPELINES IN PRODUCTION
por AD7 de ago. de 2021
Excellent!! Ver, Very Very Good. Learn a lot. Thank you for sharing.
por JZ3 de abr. de 2022
Lots of hands-on exercises accompanying knowledge learned in this course 3, but could be difficult for someone without prior working knowledge on Google Cloud platform/services.
por AP26 de abr. de 2022
It was really a wonderful and amazing course. I really learnt about what all goes in creating a successful ML project
por IA14 de jun. de 2022
There were a lot of useful information and practical insights about the subject of the course. The material on Tensorflow-specific modules felt a bit unorganized and cumbersome to go through.
Acerca de Programa especializado: Machine Learning Engineering for Production (MLOps)
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.
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