微软技术机器学习：架构与工具选择指南

Machine.Learning

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《利用微软技术进行机器学习》(Machine Learning with Microsoft Technologies)是一本实用的指南，由Leila Etaati撰写，专为读者演示如何在Microsoft技术平台上实现机器学习项目。本书的核心内容围绕如何为您的项目选择最合适的架构和工具展开，帮助您根据自身的基础设施需求和特定要求简化机器学习流程。在书中，作者强调了架构选择的重要性，因为这直接影响到开发、共享和部署机器学习解决方案的效率和效果。通过深入浅出的方式，Leila Etaati向读者展示了如何在实际操作中权衡不同的选项，如： 1. **平台选择**：书中可能涵盖了如何评估是否使用Azure Machine Learning Studio、Azure Databricks、Azure Kubernetes Service (AKS) 或者其他的云端机器学习服务，根据项目规模、数据量和团队技能来决定最佳部署环境。 2. **数据处理与集成**：讲解如何使用Azure Data Factory、SQL Server或Power BI等工具管理、清洗和整合数据源，确保数据质量和可用性对模型训练至关重要。 3. **模型开发与训练**：介绍如何利用.NET Core、Python或其他支持的编程语言（如R或Julia）与Microsoft ML.NET框架合作，构建和优化机器学习模型。 4. **模型部署与监控**：探讨如何将模型部署到Web应用程序、API或无服务器架构（如Azure Functions），以及如何设置监控机制以确保模型性能和异常处理。 5. **安全性与隐私**：讨论如何在设计架构时考虑数据安全和隐私保护措施，例如使用Azure Active Directory (AAD) 和加密技术。 6. **可扩展性和灵活性**：阐述如何构建能够适应业务变化的架构，使得机器学习解决方案能够随着时间和需求的增长而扩展。 7. **持续集成与持续部署(CI/CD)**：介绍如何使用Azure DevOps或其他DevOps工具链来自动化模型开发过程，加速迭代速度。 8. **案例研究与实战演练**：书中可能包含多个实际项目的例子，展示如何应用所学知识解决具体问题，使读者能更好地理解和应用这些概念。《利用微软技术进行机器学习》是一本实践导向的教程，不仅提供理论指导，还提供了丰富的实践技巧和策略，帮助读者在Microsoft技术生态系统中成功构建和管理机器学习项目。无论你是初学者还是经验丰富的开发者，这本书都是在Microsoft平台上提升机器学习能力的重要参考资料。

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Academics and authors have proposed different definitions of machine learning.

For example, Sebastian Raschka defined machine learning as tools for making sense

of data, using algorithms [2]. He mentioned that we encounter a significant amount of

structured (numbers) and unstructured (image, voice text, and so forth) data. Gaining

insight from these data affects the decision-making process and helps managers to

achieve a better understanding of what happened, why it happened, what will happen in

future, and how to make it happen.

The concepts of machine learning are based on discovering common patterns from

current data sets. Historically, we created reports and software to understand what

happened in the past. Analyzing recent events and data always helps us to perform

further analysis, such as finding key performance indicators (KPIs), and so forth.

Figure 1-1. A Venn diagram showing the AI category and subcategories [1]

Chapter 1 IntroduCtIon toMaChIne LearnIng

Machine Learning Approaches

There are two main approaches to machine learning:

Supervised Learning

Unsupervised Learning

In the following paragraphs, a brief explanation of each is provided. In Chapter 5,

I will go into more detail about these and offer examples of both.

Supervised Learning

The primary goal of supervised learning is to learn how to predict a group or value from

past data. By another definition, supervised learning is the machine learning task of

inferring a function from labeled training data [2].

There are different approaches to supervised learning. One is to predict a value, for

example, predicting the number of subscribers to a video channel, which could range

from one to millions of individuals. Supervised learning makes it possibile to predict

how many people will subscribe to this channel. Another example is predicting sales

for a forthcoming year that could range from $1,000 to $200,000. By this approach, an

algorithm predicts the number of sales for the company. We call this method a regression

approach, in which the outcome is a continuous value.

The other approach to supervised learning involves predicting a group, for example,

predicting whether a customer will stay with a company or leave it. In this example, the

goal is to predict whether a current customer will remain with a group. Another case

could consider a company that provides different tiers of customers, such as gold, silver,

and bronze. In this case, the supervised learning approach might predict whether a

new customer will belong to the gold, silver, or bronze group. In this type of supervised

learning, the prediction column should be a discrete class label.

Unsupervised Learning

In supervised learning, we already have an idea of the answer before creating and

training the model. In unsupervised learning, we do not predict a column, and we do not

attach any label to the data. The main goal of unsupervised learning is to find the natural

data pattern, to explore the data and extract its meaningful information.

Chapter 1 IntroduCtIon toMaChIne LearnIng

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