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)
oferecido por

Informações sobre o curso
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Habilidades que você terá
- TensorFlow Serving
- Model Monitoring
- Model Registries
- Machine Learning Operations (MLOps)
- Generate Data Protection Regulation (GDPR)
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
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
Week 1: Model Serving: Introduction
Learn how to make your ML model available to end-users and optimize the inference process
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
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
Week 4: Model Monitoring and Logging
Establish procedures to detect model decay and prevent reduced accuracy in a continuously operating production system
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
A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents
Really enjoyed it however to get he most out of it, the time commitment is large
It's intense, applied, concrete and to the point. A very good course.
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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