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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ibm-ai-workflow-ai-production
课程评论：没有评论
课程名称：AI工作流：AI在生产中的应用 课程概述：本课程是IBM AI企业工作流认证专业化的第六个课程。强烈建议按顺序完成这些课程，因为它们是一个互相关联的工作流程，每门课程都在前一门的基础上构建。课程的重点是模型在假设的流媒体公司中的生产应用。将介绍IBM Watson机器学习，学习如何在Docker容器中构建自己的API，并利用Kubernetes管理这些容器。课程还介绍了IBM生态系统中的多种工具，帮助部署或维护生产中的模型。AI工作流不是线性过程，因此课程中会有时间专注于重要的反馈回路，以促进整个工作流程的高效迭代。 完成本课程后，您将能够： 1. 使用Docker部署Flask应用程序 2. 部署简单的用户界面，以集成机器学习模型、Watson NLU和Watson视觉识别 3. 讨论基本的Kubernetes术语 4. 在Kubernetes上部署可扩展的Web应用程序 5. 讨论AI工作流中的不同反馈回路 6. 在模型生产的背景下讨论单元测试的使用 7. 使用IBM Watson OpenScale评估生产机器学习模型的偏差和性能 适合人群：本课程主要面向已经具备构建机器学习模型经验的数据科学从业者，意图深化在大型企业中构建和部署AI的技能。如果您是有志的数据科学家，本课程可能不适合您，因为您需要有实际的专业知识来从课程内容中获益。 掌握的技能：假定您已经完成了IBM AI企业工作流专业化的1至5门课程，并且在开始本课程之前，需要对以下主题有扎实的理解： - 线性代数的基本理解 - 抽样、概率理论和概率分布的理解 - 描述性和推理统计概念的知识 - 机器学习技术和最佳实践的总体理解 - Python及常用数据科学包（如NumPy、Pandas、matplotlib、scikit-learn）的实践理解 - 熟悉IBM Watson Studio - 熟悉设计思维过程 课程大纲： 1. 反馈回路与监控 - 重点关注反馈回路和监控，讨论单元测试和商业价值等重要考量，完成有关性能监控的案例研究。 2. OpenScale和Kubernetes实践 - 完成Watson OpenScale和Kubernetes的实践教程，了解如何使用这些工具跟踪生产AI的性能及其对商业目标的影响。 3. 顶点项目：整合所有内容（第一部分） - 开始为期三部分的顶点项目，回顾所学内容，旨在模拟真实世界场景的实际应用。 4. 顶点项目：整合所有内容（第二部分） - 完成顶点项目，包括构建和选择最佳模型，进行时间序列算法预测，并创建后生产分析脚本，提交项目以供同行评审。 通过本课程，您将获得在实际生产环境中有效实施和监控AI模型的技能，助力于您的职业发展。
Part: 1
Title:Feedback loops and Monitoring
Description:This module focuses on feedback loops and monitoring. Feedback loops represent all the possible ways you can return to an earlier stage in the AI enterprise workflow. We initially discussed feedback loops in the first course of this specialization; however, here our focus is on unit testing. We are also looking at business value, a very important consideration that often gets overlooked; is the model having as significant effect on business metrics as intended? It is important to be able to use log files that have been standardized across the team to answer questions about business value as well as performance monitoring. You will have an opportunity to complete a case study on performance monitoring, where you will write unit tests for a logger and a logging API endpoint, test them, and write a suite of unit tests to validate if the logging is working correctly.
Part: 2
Title:Hands on with Openscale and Kubernetes
Description:This module will wrap up the formal learning in this course by completing hands on tutorials of Watson Openscale and Kubernetes. IBM Watson OpensScale is a suite of services that allows you to track the performance of production AI and its impact on business goals, with actionable metrics, in a single console. Kubernetes is a container orchestration platform for managing, scheduling and automating the deployment of Docker containers. The containers we have developed as part of this course are essentially microservices meant to be deployed as cloud native applications.
Part: 3
Title:Capstone: Pulling it all together (Part 1)
Description:In this module you start part one (Data Investigation) of a three-part capstone project designed to pull everything you have learned together. We have provided a brief review of what you should have learned thus far; however, you may want to review the first five courses prior to starting the project. A major goal of this capstone is to emulate a real-world scenario, so we won’t be providing notebooks to guide you as we have done with the previous case studies.
Part: 4
Title:Capstone: Pulling it all together (Part 2)
Description:In this module you will complete your capstone project and submit it for peer review. Part 2 of the Capstone project involves building models and selecting the best model to deploy. You will use time-series algorithms to predict future values based on previously observed values over time. In part 3 of the Capstone project, your focus will be creating a post-production analysis script that investigates the relationship between model performance and the business metrics aligned with the deployed model. After completing and submitting your capstone project, you will have access to the solution files for further review.
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
