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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-model-deployment
课程评论：没有评论
课程名称：AI工作流程：企业模型部署 课程概述： 这是IBM AI企业工作流程认证专业的第五门课程。强烈建议按顺序完成这些课程，因为它们并非独立课程，而是互相衔接，逐步构建的工作流程。本课程介绍了数据科学家很少接触到的领域：在大型企业中部署模型。课程将重点讲解Apache Spark这一常用的机器学习模型运行框架的最佳实践，包括数据操作、模型训练和模型调优。课程中还将创建和部署一个推荐系统，并介绍模型部署技术。 课程目标： 完成本课程后，您将能够： 1. 使用Apache Spark的RDD、数据框和管道。 2. 运用spark-submit脚本与Spark环境进行交互。 3. 解释协同过滤和基于内容的过滤的工作原理。 4. 使用Apache Spark和Apache Spark流构建数据摄取管道。 5. 分析Apache Spark上机器学习模型的超参数。 6. 使用Apache Spark机器学习接口部署机器学习算法。 7. 从Watson Studio将机器学习模型部署到Watson Machine Learning。 适合人群： 本课程针对已有机器学习模型构建经验的数据科学从业者，旨在深化他们在大型企业中构建和部署人工智能的技能。如果您是有意的 数据科学家，本课程则不适合您，因为您需要具备实际的工作经验才能从课程内容中获益。 所需技能： 课程假定您已完成IBM AI企业工作流程专业的前四门课程，并具备以下基本知识： - 线性代数的基础理解 - 概率论和概率分布的理解 - 描述性和推断统计概念的知识 - 机器学习技术和最佳实践的一般理解 - Python及数据科学常用包（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： 第1部分：模型部署 描述：如今，数据科学家有更多的工具来创建基于模型或算法的解决方案，了解何时进行代码优化至关重要。本周我们将进行大量实践活动，从与Apache Spark交互开始，逐步进行Docker教程，最后进行Watson Machine Learning的实践教程。 第2部分：使用Spark部署模型 描述：本周主要集中在使用Spark部署模型，转向Spark的理由通常与规模有关，无论是在模型训练还是在预测阶段。虽然构建Spark应用程序的资源较少，但Spark为我们提供了在可扩展环境中构建的能力。我们还将研究推荐系统，现代推荐系统通常利用显式（如数字评分）和隐式（如点赞、购买、跳过、收藏）模式。大多数现代推荐系统采用协同过滤或基于内容的方法，但也存在许多其他方法和混合模式，使得某些已实现的系统难以分类。本周的最后，我们将进行关于模型部署的实操案例研究。
Part: 1
Title:Deploying Models
Description:Today data scientists have more tooling than ever before to create model-driven or algorithmic solutions, and it is important to know when to take the time to make code optimizations. This week we spend a lot of time performing hands on activities. We start this week by interacting with Apache Spark then progressing to a tutorial with Docker. We’ll wrap up the week working through a tutorial on Watson Machine Learning.
Part: 2
Title:Deploying Models using Spark
Description:This week is primarily focused on deploying models using Spark. The rationale to move to Spark almost always has to do with scale, either at the level of model training or at the level of prediction. Although the resources available to build Spark applications are fewer than those for scikit-learn, Spark gives us the ability to build in an entirely scaleable environment. We will also look at recommendation systems. Most recommender systems today are able to leverage both explicit (e.g. numerical ratings) and implicit (e.g. likes, purchases, skipped, bookmarked) patterns in a ratings matrix. The majority of modern recommender systems embrace either a collaborative filtering or a content-based approach. A number of other approaches and hybrids exist making some implemented systems difficult to categorize. We wrap the week up with our hands-on case study on Model Deployment.
This is the fifth 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 introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning 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 4 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.
