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 faz parte do Programa de cursos integrados Machine Learning Engineering for Production (MLOps)
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oferecido por
Informações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível avançado
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 26 horas para completar
Inglês
Legendas: Inglês, Francês
O que você vai aprender
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 você terá
- Explainable AI
- Fairness Indicators
- automl
- Model Performance Analysis
- Precomputing Predictions
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível avançado
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 26 horas para completar
Inglês
Legendas: Inglês, Francês
oferecido por
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - O que você aprenderá com este curso
7 horas para concluir
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 concluir
10 vídeos (Total 40 mín.), 4 leituras, 6 testes
5 horas para concluir
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 concluir
13 vídeos (Total 91 mín.), 6 leituras, 3 testes
5 horas para concluir
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 concluir
6 vídeos (Total 57 mín.), 4 leituras, 4 testes
6 horas para concluir
Week 4: Model Analysis
Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.
6 horas para concluir
12 vídeos (Total 69 mín.), 8 leituras, 5 testes
Avaliações
- 5 stars71,74%
- 4 stars17,93%
- 3 stars6,27%
- 2 stars2,24%
- 1 star1,79%
Principais avaliações do 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 AK6 de ago de 2021
Some of the topics were too advanced and instructor assumes that we know those basics. It felt rush through little bit and more of reading slides then explaining at many places
por MB20 de out de 2021
I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.
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.
Sobre Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
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