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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-modeling-pipelines-in-production
课程评论：没有评论
课程名称：生产环境中的机器学习建模管道 课程概述：在《机器学习工程的生产专业化》中的第三门课程中，您将为不同的服务环境构建模型；实施工具和技术以有效管理建模资源，最佳地满足离线和在线推理请求；并使用分析工具和性能指标来解决模型公平性、可解释性问题并缓解瓶颈。理解机器学习和深度学习的概念是必要的，但如果您希望在人工智能领域建立有效的职业生涯，您还需要具备生产工程能力。机器学习工程为生产结合了机器学习的基础概念和现代软件开发与工程角色的功能专长，帮助您发展为生产就绪的技能。 课程大纲： 第1周：神经架构搜索 描述：学习如何有效搜索适合不同服务需求的最佳模型，同时限制模型复杂性和硬件要求。 第2周：模型资源管理技术 描述：学习如何在生产环境中优化和管理模型在整个生命周期所需的计算、存储和输入/输出资源。 第3周：高性能建模 描述：实现分布式处理和并行技术，以充分利用计算资源高效训练模型。 第4周：模型分析 描述：使用模型性能分析来调试和修复模型，并测量其稳健性、公平性和稳定性。 第5周：可解释性 描述：了解模型的可解释性，即向普通人和专家观众解释模型内部工作原理的关键，以及如何促进公平性并帮助解决不同用例的监管和法律要求。
Part: 1
Title:Week 1: Neural Architecture Search
Description:Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.
Part: 2
Title:Week 2: Model Resource Management Techniques
Description:Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.
Part: 3
Title:Week 3: High-Performance Modeling
Description:Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.
Part: 4
Title:Week 4: Model Analysis
Description:Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.
Part: 5
Title:Week 5: Interpretability
Description:Learn about model interpretability - the key to explaining your model’s inner workings to laypeople and expert audiences and how it promotes fairness and helps address regulatory and legal requirements for different use cases.
In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. 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: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability
