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.
이 강좌는 Machine Learning Engineering for Production (MLOps) 특화 과정의 일부입니다.
제공자:


이 강좌에 대하여
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
귀하가 습득할 기술
- 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)
제공자:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
강의 계획표 - 이 강좌에서 배울 내용
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
검토
- 5 stars70.58%
- 4 stars21.39%
- 3 stars3.74%
- 2 stars2.67%
- 1 star1.60%
DEPLOYING MACHINE LEARNING MODELS IN PRODUCTION의 최상위 리뷰
Broad overview of the many tools and techniques for real world ML ops
A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents
It's intense, applied, concrete and to the point. A very good course.
it's a pretty good overview, only downside is the focus on GCP
Machine Learning Engineering for Production (MLOps) 특화 과정 정보
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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