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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/deploying-machine-learning-models-in-production
课程评论：没有评论
课程名称：在生产环境中部署机器学习模型 课程概述：在《机器学习工程与生产》专业化的第四门课程中，您将学习如何部署机器学习模型并使其可供最终用户使用。您将构建可扩展和可靠的硬件基础设施，以根据用例提供实时和批量推理请求。您还将实施工作流自动化和渐进式交付，以符合当前的MLOps实践，从而保持您的生产系统的正常运行。此外，您将持续监控系统，以检测模型衰退、修复性能下降，并避免系统故障，以确保其持续正常运行。 理解机器学习和深度学习的概念至关重要，但如果您希望建立有效的人工智能职业生涯，您还需要具备生产工程的能力。机器学习工程与生产结合了机器学习的基础概念与现代软件开发和工程角色的功能专长，帮助您发展面向生产的技能。 课程大纲： 第一周：模型服务简介 描述：学习如何使您的机器学习模型可供最终用户使用，并优化推理过程。 第二周：模型服务模式和基础设施 描述：学习如何通过构建可扩展和可靠的基础设施来提供模型，并交付批量和实时推理结果。 第三周：模型管理和交付 描述：学习如何实现符合现代MLOps实践的机器学习流程、管道和工作流自动化，这将使您能够在项目的整个生命周期内进行管理和审计。 第四周：模型监控和日志记录 描述：建立程序以检测模型的衰退，防止在持续运行的生产系统中降低准确性。
Part: 1
Title:Week 1: Model Serving: Introduction
Description: Learn how to make your ML model available to end-users and optimize the inference process
Part: 2
Title:Week 2: Model Serving: Patterns and Infrastructure
Description:Learn how to serve models and deliver batch and real-time inference results by building scalable and reliable infrastructure
Part: 3
Title:Week 3: Model Management and Delivery
Description: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
Part: 4
Title:Week 4: Model Monitoring and Logging
Description:Establish procedures to detect model decay and prevent reduced accuracy in a continuously operating production system
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. 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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving Introduction Week 2: Model Serving Patterns and Infrastructures Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging
