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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops
课程评论：没有评论
课程名称：生产中的机器学习工程（MLOps） 课程概述： 本课程将帮助您设计一个端到端的机器学习生产系统，包括项目范围、数据需求、建模策略和部署要求。您将学习如何建立模型基线、处理概念漂移，并原型开发、部署和持续改进一个实际应用的机器学习模型。此外，您还将学习如何构建数据管道，包括收集、清理和验证数据集，并利用数据血统和来源元数据工具建立数据生命周期。课程还将介绍最佳实践和渐进式交付技术，以维护和监控一个持续运行的生产系统。 您将获得的技能包括： - 管理机器学习生产系统 - 部署管道 - 模型管道 - 数据管道 - 机器学习工程在生产中的应用 - 人工水平表现（HLP） - 概念漂移 - 模型基线 - 项目范围和设计 - 机器学习部署挑战 - 机器学习元数据 - 卷积神经网络 课程特色： 该特训课程共吸引了80,874次浏览。理解机器学习和深度学习的概念是必要的，但如果您希望构建一个有效的AI职业生涯，您需要具备生产工程能力。有效地部署机器学习模型需要在软件工程和DevOps等技术领域较为常见的能力。 “生产中的机器学习工程（MLOps）”特训课程涵盖了如何构思、构建和维护在生产中持续运作的集成系统。与标准的机器学习建模相比，生产系统需要处理不断变化的数据，且需在最低成本下实现最佳性能。您将学习如何有效有效地使用成熟的工具和方法论来完成这一切。 通过实践学习项目，您将在课程结束时能够： - 设计完整的ML生产系统，包括项目范围、数据需求、建模策略和部署要求 - 建立模型基线、处理概念漂移，并开发、部署、持续改进一个实际的ML应用程序 - 构建数据管道，收集、清理和验证数据集 - 实施特征工程、转换和选择，使用TensorFlow Extended - 利用数据血统和来源元数据工具建立数据生命周期，遵循企业数据架构中的数据演变 - 管理建模资源，并最佳服务于离线/在线推理请求 - 应用分析技术解决模型公平性、可解释性问题，并减轻瓶颈 - 提供需要不同基础设施的模型服务的部署管道 - 应用最佳实践和渐进式交付技术，维护持续运行的生产系统 课程要求： - 对AI/深度学习有一定的了解 - 具备中级Python技能 - 有使用任一深度学习框架（如PyTorch、Keras或TensorFlow）的经验 完成时间：大约需要4个月，建议每周学习6小时。 可用语言：英语（提供英语和法语字幕） 证书：完成课程后可获得可分享的证书。 课程链接：[点击这里](https://www.coursera.org/learn/introduction-to-machine-learning-in-production)
Course Link: https://www.coursera.org/learn/introduction-to-machine-learning-in-production
Name:Introduction to Machine Learning in Production
Description:Offered by DeepLearning.AI. In the first course of Machine Learning Engineering for Production Specialization, you will identify the various ... Enroll for free.
Course Link: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production
Name:Machine Learning Data Lifecycle in Production
Description:Offered by DeepLearning.AI. In the second course of Machine Learning Engineering for Production Specialization, you will build data ... Enroll for free.
Course Link: https://www.coursera.org/learn/machine-learning-modeling-pipelines-in-production
Name:Machine Learning Modeling Pipelines in Production
Description:Offered by DeepLearning.AI. In the third course of Machine Learning Engineering for Production Specialization, you will build models for ... Enroll for free.
Course Link: https://www.coursera.org/learn/deploying-machine-learning-models-in-production
Name:Deploying Machine Learning Models in Production
Description:Offered by DeepLearning.AI. In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ... Enroll for free.
What you will learn
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
Skills you will gain
Managing Machine Learning Production Systems
Deployment Pipelines
Model Pipelines
Data Pipelines
Machine Learning Engineering for Production
Human-level Performance (HLP)
Concept Drift
Model baseline
Project Scoping and Design
ML Deployment Challenges
ML Metadata
Convolutional Neural Network
About this Specialization
80,874
recent views
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.
Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.
The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.
In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.
Applied Learning Project
By the end, you'll be ready to
• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
• Build data pipelines by gathering, cleaning, and validating datasets
• Implement feature engineering, transformation, and selection with TensorFlow Extended
• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
• Apply techniques to manage modeling resources and best serve offline/online inference requests
• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
• Deliver deployment pipelines for model serving that require different infrastructures
• Apply best practices and progressive delivery techniques to maintain a continuously operating production system
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Advanced Level
• Some knowledge of AI / deep learning
• Intermediate skills in Python
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Hours to complete
Approximately 4 months to complete
Suggested pace of 6 hours/week
Available languages
English
Subtitles: English, French
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Advanced Level
• Some knowledge of AI / deep learning
• Intermediate skills in Python
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Hours to complete
Approximately 4 months to complete
Suggested pace of 6 hours/week
Available languages
English
Subtitles: English, French
