使用Azure机器学习进行预测分析实战指南

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"Predictive Analytics with Microsoft Azure Machine Learning (Apress, 2nd ed, 2015)" 是一本面向数据科学和机器学习实践的教程书籍，专注于构建和部署预测模型。书中深入介绍了2015年2月18日发布的微软Azure Machine Learning服务，提供了构建推荐系统、倾向性模型以及客户流失和预测性维护模型的实用指导。本书通过任务导向的描述和具体的端到端示例，使读者能够立即开始使用这项新服务。从数据输入到应用机器学习，再到评估模型和将其部署为Web服务，涵盖了该服务的所有方面。书中的内容旨在帮助读者快速构建和部署使用Azure Machine Learning的复杂预测模型。作者Roger Barga、Valentine Fontama和Wee Hyong Tok在书中详细讲解了如何利用Azure Machine Learning的特性，包括但不限于数据预处理、特征工程、模型选择、训练、验证、调优和模型部署。此外，还可能涉及了相关的算法，如决策树、随机森林、支持向量机、神经网络和回归分析等。书中可能还包括了如何使用Azure Machine Learning Studio进行实验设计，以及如何将训练好的模型打包成Web服务，以便于其他应用程序或者系统集成使用。此外，书中的案例研究可能覆盖了各种业务场景，如零售业的客户购买行为预测、电信行业的用户流失预测、制造业的设备故障预测等。这些实例有助于读者理解如何将理论知识应用于实际问题解决，并提升在数据分析和预测建模方面的技能。通过阅读这本书，读者不仅可以掌握Azure Machine Learning的基本操作，还能了解预测分析的整个流程，包括数据准备、模型构建、模型评估和部署。对于想要进入或已经从事数据科学和机器学习领域的专业人士来说，这本书是理解和应用Azure Machine Learning的宝贵资源。

■ about the teChniCal reviewers

xvi

Jacob has held analytics leadership positions at FICO, SAS, ID Analytics, and

boutique consulting company BasePoint. He holds BS and MS degrees in Electrical

Engineering from the University of Pretoria, and a PhD in Computer Science from the

University of Southern California.

He and his wife, Tanya, have two boys, aged 10 and 12. They enjoy camping, hiking,

and snow sports. Jacob is a private pilot and is constantly looking for excuses to go flying.

Dr. Hang Zhang joined Microsoft in May 2014 as

a Senior Data Scientist, Cloud Machine Learning

Data Science. Before joining Microsoft, Hang was a

Staff Data Scientist at WalmartLabs leading a team

building internal tools for search analytics and

business intelligence. He worked for two years in Opera

Solutions as a Senior Scientist focusing on machine

learning and data science between 2011 and 2013.

Before that, Hang worked at Arizona State University

for four years in the area of neuro-informatics. Hang

holds a Ph.D. degree in Industrial and Systems

Engineering, and a M.S. degree in Statistics from

Rutgers, The State University of New Jersey.

Dr. Yan Zhang is a senior data scientist in Microsoft

Cloud & Enterprise Azure Machine Learning product

team. She builds predictive models and generalized

data driven solutions on the Cloud machine

learning platform. Her recent research includes

predictive maintenance in IoT applications, customer

segmentation, and text mining. Dr. Zhang received her

Ph.D. in data mining. Before joining Microsoft, she was

a research faculty at Syracuse University, USA.

www.it-ebooks.info

xix

Foreword

Few people appreciate the enormous potential of machine learning (ML) in enterprise

applications. I was lucky enough to get a taste of its potential benefits just a few

months into my first job. It was 1995, and credit card issuers were beginning to adopt

neural network models to detect credit card fraud in real time. When a credit card is

used, transaction data from the point of sale system is sent to the card issuer's credit

authorization system where a neural network scores for the probability of fraud. If the

probability is high, the transaction is declined in real time. I was a scientist building such

models, and one of my first model deliveries was for a South American bank. When the

model was deployed, the bank identified over a million dollars of previously undetected

fraud on the very first day. This was a big eye-opener. In the years since, I have seen

ML deliver huge value in diverse applications such as demand forecasting, failure and

anomaly detection, ad targeting, online recommendations, and virtual assistants like

Cortana. By embedding ML into their enterprise systems, organizations can improve

customer experience, reduce the risk of systemic failures, grow revenue, and realize

significant cost savings.

However, building ML systems is slow, time-consuming, and error prone. Even

though we are able to analyze very large data sets these days and deploy at very high

transaction rates, the following bottlenecks remain:

• ML system development requires deep expertise. Even though

the core principles of ML are now accessible to a wider audience,

talented data scientists are as hard to hire today as they were two

decades ago.

• Practitioners are forced to use a variety of tools to collect, clean,

merge, and analyze data. These tools have a steep learning curve

and are not integrated. Commercial ML software is expensive to

deploy and maintain.

• Building and verifying models requires considerable

experimentation. Data scientists often find themselves limited by

compute and storage because they need to run a large number of

experiments that generate considerable new data.

• Software tools do not support scalable experimentation or

methods for organizing experiment runs. The act of collaborating

with a team on experiments and sharing derived variables,

scripts, etc. is manual and ad-hoc without tools support.

Evaluating and debugging statistical models remains a challenge.

www.it-ebooks.info

■ Foreword

Data scientists work around these limitations by writing custom programs and by

doing undifferentiated heavy lifting as they perform their ML experiments. But it gets

harder in the deployment phase. Deploying ML models in a mission-critical business

process such as real-time fraud prevention or ad targeting requires sophisticated

engineering. The following needs must be met:

• Typically, ML models that have been developed offline now have

to be reimplemented in a language such as C++, C#, or Java.

• The transaction data pipelines have to be plumbed. Data

transformations and variables used in the offline models have to

be recoded and compiled.

• These reimplementations inevitably introduce bugs, requiring

verification that the models work as originally designed.

• A custom container for the model has to be built, with appropriate

monitors, metrics, and logging.

• Advanced deployments require A/B testing frameworks to

evaluate alternative models side by side. One needs mechanisms

to switch models in or out, preferably without recompiling and

deploying the entire application.

• One has to validate that the candidate production model works as

originally designed through statistical tests.

• The automated decisions made by the system and the business

outcomes have to be logged for refining the ML models and for

monitoring.

• The service has to be designed for high availability, disaster

recovery, and geo-proximity to end points.

• When the service has to be scaled to meet higher transaction

rates and/or low latency, more work is required to provision new

hardware, deploy the service to new machines, and scale out.

These are time-consuming and engineering-intensive steps, expensive in terms of

both infrastructure and manpower. The end-to-end engineering and maintenance of a

production ML application requires a highly skilled team that few organizations can build

and sustain.

Microsoft Azure ML was designed to solve these problems.

• It’s a fully managed cloud service with no software to install,

no hardware to manage, and no OS versions or development

environments to grapple with.

• Armed with nothing but a browser, data scientists can log on to

Azure and start developing ML models from any location, from

any device. They can host a practically unlimited number of files

on Azure storage.

www.it-ebooks.info

■ Foreword

xxi

• ML Studio, an integrated development environment for ML,

lets you set up experiments as simple data flow graphs, with an

easy-to-use drag, drop, and connect paradigm. Data scientists

can avoid programming for a large number of common tasks,

allowing them to focus on experiment design and iteration.

• Many sample experiments are provided to make it easy to get

started.

• A collection of best-of-breed algorithms developed by Microsoft

Research is built in, as is support for custom R code. Over 350

open source R packages can be used securely within Azure ML.

• Data flow graphs can have several parallel paths that

automatically run in parallel, allowing scientists to execute

complex experiments and make side-by-side comparisons

without the usual computational constraints.

• Experiments are readily sharable, so others can pick up on your

work and continue where you left off.

Azure ML also makes it simple to create production deployments at scale in the

cloud. Pretrained ML models can be incorporated into a scoring workflow and, with

a few clicks, a new cloud-hosted REST API can be created. This REST API has been

engineered to respond with low latency. No reimplementation or porting is required,

which is a key benefit over traditional data analytics software. Data from anywhere on

the Internet (laptops, websites, mobile devices, wearables, and connected machines)

can be sent to the newly created API to get back predictions. For example, a data

scientist can create a fraud detection API that takes transaction information as input

and returns a low/medium/high risk indicator as output. Such an API would then be

“live” on the cloud, ready to accept calls from any software that a developer chooses to

call it from. The API backend scales elastically, so that when transaction rates spike, the

Azure ML service can automatically handle the load. There are virtually no limits on the

number of ML APIs that a data scientist can create and deploy–and all this without any

dependency on engineering. For engineering and IT, it becomes simple to integrate a

new ML model using those REST APIs, and testing multiple models side-by-side before

deployment becomes easy, allowing dramatically better agility at low cost. Azure provides

mechanisms to scale and manage APIs in production, including mechanisms to measure

availability, latency, and performance. Building robust, highly available, reliable ML

systems and managing the production deployment is therefore dramatically faster,

cheaper, and easier for the enterprise, with huge business benefits.

We believe Azure ML is a game changer. It makes the incredible potential of ML

accessible both to startups and large enterprises. Startups are now able to use the same

capabilities that were previously available to only the most sophisticated businesses.

Larger enterprises are able to unleash the latent value in their big data to generate

significantly more revenue and efficiencies. Above all, the speed of iteration and

experimentation that is now possible will allow for rapid innovation and pave the way for

intelligence in cloud-connected devices all around us.

www.it-ebooks.info

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