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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ml-models-human-in-the-loop-pipelines
课程评论：没有评论
课程名称：优化机器学习模型及部署人机协作流程 课程概述：在“实用数据科学专业化”系列的第三门课程中，您将学习一系列提高模型性能和降低成本的技术，自动调整模型准确性，比较预测性能，并使用人类智能生成新的训练数据。通过使用亚马逊SageMaker的超参数调整（HPT），您将调优文本分类器，并将两个模型候选者部署到A/B测试中，以比较它们的实时预测性能，并使用亚马逊SageMaker托管自动扩展获胜模型。最后，您将建立一个人机协作的流程，以修复错误分类的预测，并使用亚马逊增强AI和亚马逊SageMaker Ground Truth生成新的训练数据。 实用数据科学旨在处理无法放入本地硬件的庞大数据集，这些数据集可能来自多个来源。在云中开发和运行数据科学项目的一个最大优势是云提供的灵活性和弹性，能够以最低的成本进行扩展。 “实用数据科学专业化”帮助您掌握有效部署数据科学项目的实用技能，并在每个机器学习工作流程阶段克服挑战，使用亚马逊SageMaker。该专业化课程专为熟悉Python和SQL编程语言的数据开发者、科学家和分析师设计，旨在教您如何在AWS云中构建、训练和部署可扩展的端到端机器学习管道，包括自动化和人机协作的流程。 课程大纲： 第一部分： 标题：第1周：高级模型训练、调优和评估 描述：使用数据并行和模型并行策略以及自动模型调优训练、调优和评估模型。 第二部分： 标题：第2周：高级模型部署和监控 描述：通过A/B测试部署模型，监控模型性能，并从基线指标中检测漂移。 第三部分： 标题：第3周：数据标注和人机协作流程 描述：利用私人人力资源大规模标注数据，并建立人机协作的流程。
Part: 1
Title:Week 1: Advanced model training, tuning and evaluation
Description:Train, tune, and evaluate models using data-parallel and model-parallel strategies and automatic model tuning.
Part: 2
Title:Week 2: Advanced model deployment and monitoring
Description:Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
Part: 3
Title:Week 3: Data labeling and human-in-the-loop pipelines
Description:Label data at scale using private human workforces and build human-in-the-loop pipelines.
In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.
