Data Science & Azure Machine Learning Service — An introduction

数据科学和Azure机器学习服务-简介

Background

背景

I have been practicing data science — developed ML pipelines for Financial services, Pharmaceuticals, Health Care and Consulting domains. I have developed both Machine learning and Deep learning models. I have recently cleared DP-100 Microsoft Azure data scientist certification. As part of my passion for continuous learning, I am planning to write a series of articles on data science, covering machine learning and deep learning, from Microsoft Azure Machine Learning Service point of view.

我一直在练习数据科学-为金融服务，制药，保健和咨询领域开发的ML管道。 我已经开发了机器学习和深度学习模型。 我最近通过了DP-100 Microsoft Azure数据科学家认证。 作为对持续学习的热情的一部分，我计划从Microsoft Azure机器学习服务的角度撰写一系列有关数据科学的文章，涵盖机器学习和深度学习。

Data Science — A generic overview

数据科学-概述

First, a brief synopsis on data science. In a nutshell, data science is all about data — it facilitates understanding data from statistical point of view. In general, data science is an intense quantitative field, requiring strong fundamentals in mathematics, statistics, computational, data structures & programming. In today’s data driven world, data science broadly supports three inter-related areas: AI, Machine Learning, and Deep Learning. Their relationship is shown below:

首先，简要介绍一下数据科学。 简而言之，数据科学就是关于数据的，它有助于从统计角度理解数据。 通常，数据科学是一个激烈的定量领域，需要在数学，统计，计算，数据结构和编程方面具有强大的基础知识。 在当今数据驱动的世界中，数据科学广泛支持三个相互关联的领域：人工智能，机器学习和深度学习。 它们的关系如下所示：

With this simple background, without much ado, let us focus on our topic of interest: how do we leverage feature-rich powerful Azure Machine Learning to solve our machine learning business cases.

凭着这样简单的背景，我们毫不费力地将注意力集中在我们感兴趣的主题上：我们如何利用功能丰富且功能强大的Azure机器学习来解决我们的机器学习业务案例。

Azure Machine Learning

Azure机器学习

So, what is Azure Machine Learning? It provides a cloud-based platform for training, deploying, and managing ML models. It is a platform for operating machine learning workloads in the cloud. Azure Machine Learning supports all activities associated with a typical machine learning business case:

那么，什么是Azure机器学习？ 它提供了一个基于云的平台，用于训练，部署和管理ML模型。 它是用于在云中操作机器学习工作负载的平台。 Azure机器学习支持与典型机器学习业务案例相关的所有活动：

1. Understanding data了解数据
2. Data preprocessing, including featurization数据预处理，包括功能化
3. Descriptive statistics描述性统计
4. Model building建筑模型
5. Model tuning — Hyperparameters模型调整—超参数
6. Model evaluation模型评估
7. Model deployment (MLOps pipeline)模型部署(MLOps管道)

In addition, it supports other powerful and useful features such as Data Drift. Data drift involves identifying issues with data changes over a period of time on the trained model. Taking timely action using data drift metrics helps avoiding many pitfalls that data scientists face in today’s production set up.

此外，它还支持其他强大而有用的功能，例如数据漂移。 数据漂移涉及在经过训练的模型上确定一段时间内数据变化的问题。 使用数据漂移指标及时采取行动有助于避免数据科学家在当今生产环境中面临的许多陷阱。

Azure ML Architecture

Azure ML体系结构

The following diagram shows Azure Machine Learning architecture:

下图显示了Azure机器学习架构：

As you can see in the above diagram, Azure Machine Learning (henceforth: Azure ML) leverages its resource horsepower from Azure cloud. This combination of cloud and data science makes it extremely powerful to design and train resource intensive machine learning models. Particularly, when a machine learning model is intense (in terms of computation. Ex: Boosting, neural networks, etc.) with sufficient loads of data, cloud based Azure ML could supply powerful compute target to train it much faster.

如上图所示，Azure机器学习(以下简称：Azure ML)利用了来自Azure云的资源功能。 云和数据科学的这种结合使设计和训练资源密集型机器学习模型变得极为强大。 尤其是，当机器学习模型非常密集(就计算而言。例如：Boosting，神经网络等)并且有足够的数据负载时，基于云的Azure ML可以提供强大的计算目标来更快地对其进行训练。

Features supported by Azure Machine Learning

Azure机器学习支持的功能

As shown in the diagram above, out of the box, Azure ML supports following business critical features:

如上图所示，Azure ML开箱即用地支持以下关键业务功能：

1. Scalable on-demand compute: Azure ML supports many computes (aka. compute targets), including its powerful compute cluster. It is scalable auto on-demand compute usage for machine learning workloads. That means, in a business sense, you pay only for what you use.

1.可扩展的按需计算：Azure ML支持许多计算(也称为计算目标)，包括其强大的计算群集。 它是可扩展的自动按需计算用法，用于机器学习工作负载。 从商业角度讲，这意味着您只为使用的商品付费。

2. Data Storage and connectivity: Provides an easy-to-use, yet powerful data storage layer. It is so flexible, yet poweful, that data storage can be easily integrated with Python Pandas dataframe and Spark dataframe

2.数据存储和连接性：提供易于使用但功能强大的数据存储层。 它非常灵活，但功能强大，可以轻松地将数据存储与Python Pandas数据框和Spark数据框集成。

3. Metrics and monitoring: Feature is very helpful to data scientists and machine learning engineers. You can get various metrics for a given business case and often, it helps you to see how different metrics are behaving

3.指标和监控：功能对数据科学家和机器学习工程师非常有帮助。 您可以获取给定业务案例的各种指标，并且通常可以帮助您了解不同指标的行为方式

Other features such as ML orchestration using Pipeline and model registration

其他功能，例如使用管道进行ML编排和模型注册

Machine Learning features

机器学习功能

Azure Machine Learning platform provides means to implement both machine learning and deep learning. In a broad sense, it supports following machine learning types covering both supervised and unsupervised learning:

Azure机器学习平台提供了实现机器学习和深度学习的方法。 从广义上讲，它支持以下机器学习类型，包括监督学习和无监督学习：

1. Classification

1.分类

2. Regression

2.回归

3. Time series forecasting

3.时间序列预测

4. Clustering

4.聚类

5. Recommenders

5.推荐人

Two powerful Azure ML features that are quite helpful to both seasoned data scientists and those who do not have required mathematical background are:

两项功能强大的Azure ML功能对经验丰富的数据科学家和那些不需要数学背景的人都非常有帮助：

1. Designer — a drag and drop feature using Modules (see below for definition of modules). This is basically a no-code machine learning feature

1.设计器-使用模块的拖放功能(有关模块的定义，请参见下文)。 这基本上是无代码的机器学习功能

2. AutoML — Automated ML. Using this feature, you will let Azure ML choose and run group of algorithms and decide the best algorithm based on a metric. For example, the best classification algorithm could be chosen on ROC AUC curve. It is feature rich and very flexible that the user could block a list of algorithms from choosing.

2. AutoML-自动ML。 使用此功能，您可以让Azure ML选择并运行算法组，并根据指标确定最佳算法。 例如，可以在ROC AUC曲线上选择最佳分类算法。 用户可以阻止选择的算法列表功能丰富且非常灵活。

In Azure ML, a module (UI component) represents a set of code that can run independently and perform a machine learning task, given the required inputs. A module might contain a particular algorithm, or perform a task that is important in machine learning, such as missing value replacement (for data preprocessing), or statistical analysis.

在Azure ML中，模块(UI组件)表示一组代码，这些代码可以在给定所需输入的情况下独立运行并执行机器学习任务。 模块可能包含特定算法，或者执行在机器学习中很重要的任务，例如缺失值替换(用于数据预处理)或统计分析。

What Next?

接下来是什么？

This article covered summary benefits and features available in Azure Machine Learning service. In the next few articles, I will cover some of the Azure resource level topics such as Workspace, Data Storage, and so on. Ultimately, my focus is going to be covering Azure ML Service features and its support for Machine Learning and Deep Learning. I personally feel that Azure ML service in the current form is either missing some of the business critical algorithms or not covered sufficiently. I may touch these topics in the future articles.

本文介绍了Azure机器学习服务中可用的摘要优点和功能。 在接下来的几篇文章中，我将介绍一些Azure资源级别的主题，例如工作区，数据存储等。 最终，我的重点将是覆盖Azure ML服务功能及其对机器学习和深度学习的支持。 我个人认为当前形式的Azure ML服务或者缺少一些关键业务算法，或者没有被充分介绍。 在以后的文章中，我可能会涉及这些主题。

Please stay tuned for my next article on this series. In the meantime, I request readers providing suggestions / feedback for improvement.

请继续关注我关于该系列的下一篇文章。 同时，我要求读者提供建议/反馈以进行改进。