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.
Этот курс входит в специализацию ''Специализация Machine Learning Engineering for Production (MLOps)'
от партнера

Об этом курсе
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Приобретаемые навыки
- 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)
от партнера

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Программа курса: что вы изучите
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
Рецензии
- 5 stars70,58 %
- 4 stars21,39 %
- 3 stars3,74 %
- 2 stars2,67 %
- 1 star1,60 %
Лучшие отзывы о курсе DEPLOYING MACHINE LEARNING MODELS IN PRODUCTION
It's intense, applied, concrete and to the point. A very good course.
This is really a good learning with real word prodtion deployment. There are many things which we got in this learning.
The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.
I was hoping for a final project that I can use in my portfolio because the course content is so much and not easy to digest
Специализация Machine Learning Engineering for Production (MLOps): общие сведения
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.

Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
Is financial aid available?
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