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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production
课程评论：没有评论
课程名称：机器学习数据生命周期在生产中 概述：在《机器学习工程为生产专业化》的第二门课程中，您将构建数据管道，通过收集、清洗和验证数据集来评估数据质量；使用TensorFlow Extended实现特征工程、转换和选择，从数据中获得最大的预测能力；同时利用数据血缘和来源元数据工具建立数据生命周期，并通过企业数据模式跟踪数据演变。理解机器学习和深度学习的概念是必要的，但如果您希望建立有效的人工智能职业生涯，生产工程能力同样重要。机器学习工程为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，帮助您培养适合生产环境的技能。 课程大纲： 第1周：数据收集、标记和验证 第2周：特征工程、转换和选择 第3周：数据旅程和数据存储 第4周：高级数据标记方法、数据增强和不同数据类型的预处理 课程内容： 第一部分：第1周：数据收集、标记和验证 描述：本周简要介绍机器学习生产系统。更具体地说，您将学习如何利用TensorFlow Extended (TFX)库来收集、标记和验证数据，以使其准备好投入生产。 第二部分：第2周：特征工程、转换和选择 描述：通过编码结构化和非结构化数据类型，解决类别不平衡问题，使用TensorFlow Extended实现特征工程、转换和选择。 第三部分：第3周：数据旅程和数据存储 描述：了解数据在生产系统生命周期中的旅程，利用机器学习元数据和企业模式来快速应对不断演变的数据。 第四部分（可选）：第4周：高级标记、增强和数据预处理 描述：结合标记和未标记的数据以提高机器学习模型的准确性，并增强数据以多样化训练集。
Part: 1
Title:Week 1: Collecting, Labeling and Validating Data
Description:This week covers a quick introduction to machine learning production systems. More concretely you will learn about leveraging the TensorFlow Extended (TFX) library to collect, label and validate data to make it production ready.
Part: 2
Title:Week 2: Feature Engineering, Transformation and Selection
Description:Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances
Part: 3
Title:Week 3: Data Journey and Data Storage
Description:Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.
Part: 4
Title:Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing
Description:Combine labeled and unlabeled data to improve ML model accuracy and augment data to diversify your training set.
In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. 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: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types
