In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.
Este curso faz parte do Programa de cursos integrados Machine Learning Engineering for Production (MLOps)
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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. 33 horas para completar
Inglês
Legendas: Inglês, Francês
Habilidades que você terá
- TensorFlow Serving
- Model Monitoring
- Model Registries
- Machine Learning Operations (MLOps)
- Generate Data Protection Regulation (GDPR)
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. 33 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
5 horas para concluir
Week 1: Model Serving: Introduction
Learn how to make your ML model available to end-users and optimize the inference process
5 horas para concluir
7 vídeos (Total 34 mín.), 5 leituras, 5 testes
10 horas para concluir
Week 2: Model Serving: Patterns and Infrastructure
Learn how to serve models and deliver batch and real-time inference results by building scalable and reliable infrastructure
10 horas para concluir
8 vídeos (Total 44 mín.), 8 leituras, 8 testes
12 horas para concluir
Week 3: Model Management and Delivery
Learn how to implement ML processes, pipelines, and workflow automation that adhere to modern MLOps practices, which will allow you to manage and audit your projects during their entire lifecycle
12 horas para concluir
9 vídeos (Total 82 mín.), 10 leituras, 6 testes
6 horas para concluir
Week 4: Model Monitoring and Logging
Establish procedures to detect model decay and prevent reduced accuracy in a continuously operating production system
6 horas para concluir
13 vídeos (Total 66 mín.), 7 leituras, 5 testes
Avaliações
- 5 stars71,59%
- 4 stars21,89%
- 3 stars2,95%
- 2 stars2,36%
- 1 star1,18%
Principais avaliações do DEPLOYING MACHINE LEARNING MODELS IN PRODUCTION
por LG21 de dez de 2021
A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents
por EB5 de fev de 2022
Really enjoyed it however to get he most out of it, the time commitment is large
por VL16 de mai de 2022
It's intense, applied, concrete and to the point. A very good course.
por TH18 de dez de 2021
Very insightful, with a good high-level explanation of challenges surrounding model usage and deployments in a production environment.
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.
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