‌In the last 12 months, there have been myriad developments in machine learning (ML) tools and applications, and hardware for AI and ML is also progressing. Google’s TPUs are in their third generation, the AWS Inferentia chip is a year old, Intel’s Nervana Neural Network Processors are enabling deep learning, and Microsoft is reportedly developing its own customAIhardware.

This year, Algorithmia has had conversations with thousands of companies in various stages of machine learning maturity. From them we developed hypotheses about the state of machine learning in the enterprise, and in October, we decided to test those hypotheses. Building on the State of Enterprise Machine Learning report we published in 2018, we conducted a new two-prong survey this year, polling nearly 750 business decision makers across all industries from companies actively developing machine learning lifecycles or just beginning their machine learning journey.

‌在过去的 12 个月里，机器学习 (ML) 工具和应用程序有了无数的发展，人工智能和机器学习的硬件也在进步。 谷歌的 TPU 已进入第三代，AWS 推理芯片已有一年的历史，英特尔的 Nervana 神经网络处理器正在支持深度学习，据报道微软正在开发自己的定制人工智能硬件。

今年，Algorithmia 与数千家处于机器学习成熟度不同阶段的公司进行了对话。 我们从他们那里提出了关于企业机器学习状态的假设，并在 10 月决定检验这些假设。 基于我们在 2018 年发布的企业机器学习状况报告，我们今年进行了一项新的双管齐下的调查，对来自各行各业的近 750 名业务决策者进行了民意调查，这些决策者包括来自积极开发机器学习生命周期或刚刚开始机器学习之旅的公司。

One set of respondents was administered a blind version of our survey by a third-party (we refer to this group in the report as Group A); the other set was sent a survey by Algorithmia and was aware of the author (referred to herein as Group B). Group A contained 303 respondents and Group B contained 442.

We analyzed the responses from both groups for insight into their work, their companies’ machine learning roadmaps, and the changes they’ve seen in recent months with regard to ML development. Where applicable, we state when only one group is being cited in a given statistic. The Methodology section provides further detail on the specifics of the survey prongs and how we processed the data.

The following are the findings of that effort, presented with our original hypotheses, as well as our analysis of the results. Where possible, we have provided a year-on-year comparison with data from 2018 and included predictions about what is likely to manifest in the ML space in the near term. We will soon make our survey data available on an interactive webpage to foster greater understanding of the ML landscape, and we are committed to being good stewards of this technology.

Algorithmia seeks to empower every organization to achieve its full potential through the use of artificial intelligence and machine learning by delivering the last-mile solution for model deployment at scale.

其中一组受访者由第三方管理我们的盲版调查(我们在报告中将此组称为A组);另一组由Algorithmia发送一份调查问卷，并且知道作者(这里称为B组)。A组受访者为303人，B组受访者为442人。

我们分析了这两组人的反馈，以深入了解他们的工作、他们公司的机器学习路线图，以及他们在最近几个月在机器学习开发方面所看到的变化。在适用的情况下，当一个给定的统计数据中只有一个组被引用时，我们会说明。方法论部分提供了关于调查重点的细节以及我们如何处理数据的进一步详细信息。

以下是这项工作的发现，以及我们最初的假设，以及我们对结果的分析。在可能的情况下，我们提供了与2018年数据的同比比较，并对ML空间近期可能出现的情况进行了预测。我们将很快在一个交互式网页上提供我们的调查数据，以促进对ML环境的更好理解，我们致力于成为这项技术的优秀管理者。

Algorithmia旨在通过使用人工智能和机器学习，为模型大规模部署提供最后一英里的解决方案，使每个组织都能充分发挥其潜力。

The main takeaway from the 2020 State of Enterprise Machine Learning survey is that a growing number of companies are entering the early stages of ML development, but challenges in deployment, scaling,versioning, and other sophistication efforts still hinder teams from extracting value from their ML investments. As a result, we will likely see a boom in the number of ML companies providing services to overcome these obstacles in the near term.

In this report, we focus on seven key survey findings and what they say about the machine learning landscape. Those key findings are as follows:

(1)、The number of data scientist roles at companies is often less than 10, but is growing rapidly across all industries.

(2)、Business use cases for machine learning are becoming more varied but currently, customer-centric applications are the most common.

(3)、Machine learning operationalization (having a deployed ML lifecycle) is fledgling but maturing across all industries with software and IT firms leading the charge.

(4)、The main challenges people face when developing ML capabilities are scale, version control, model reproducibility, and aligning stakeholders.

(5)、The time it takes to deploy a model is stuck somewhere between 31 and 90 days for most companies.

(6)、Budgets for ML programs are growing most often by 25 percent, and the banking, manufacturing, and IT industries have seen the largest budget growth this year.

(7)、Organizations are determining ML success by both business unit and statistical metrics with a significant divide by job level.

2020 年企业机器学习现状调查的主要内容是，越来越多的公司正进入 ML 开发的早期阶段，但部署、扩展、版本控制和其他复杂工作方面的挑战，仍然阻碍团队从其 ML 投资中获取价值。 因此，我们可能会看到为克服这些障碍而提供服务的ML公司数量激增。

在本报告中，我们将重点关注七个关键的调查结果，以及它们对机器学习前景的看法。这些主要发现如下:

(1)、公司的数据科学家职位数量通常少于 10 个，但在所有行业中都在迅速增长。

(2)、机器学习的业务用例越来越多样化，但目前以客户为中心的应用最为普遍。

(3)、机器学习操作化（具有已部署的 ML 生命周期）在所有行业都处于起步阶段，但在软件和IT公司主导的所有行业中都日趋成熟。

(4)、人们在开发 ML 能力时面临的主要挑战是规模、版本控制、模型可再现性和协调利益相关者。

(5)、对于大多数公司来说，部署模型所需的时间大约在31到90天之间。

(6)、机器学习项目的预算增长幅度最大，达到 25%，其中银行、制造和 IT 行业的预算增长幅度今年最大。

(7)、组织正在通过业务部门和统计指标来确定 ML 的成功，并根据工作级别进行显著划分。

The report will go into each finding in detail and provide analysis and our outlook.

报告将详细研究每一项发现，并提供分析和展望。

One of the pieces of data we collected this year was the number of data scientists employed at the respondent’s place of work. In conversations we regularly have with companies, we repeatedly hear that management is prioritizing hiring for the data science role above many others, including engineering, IT, and software. Here is what the survey results showed.

Half of people polled (across both survey groups) said their companies employ between one and 10 data scientists. This is actually down from 2018 wherein 58 percent of companies claimed to employ between one and 10 data scientists.

We would have expected the number to increase over time because investment in AI and ML is known to be growing (Gartner). When assessed in the context of the full data, however, a likely reason for the downward trend presents itself. In 2018, 18 percent of companies employed 11 or more data scientists. This year, however, that number soared to 39 percent, suggesting that across all industries, organizations are ramping up their hiring efforts to build larger data science arsenals, with some of them starting from close to 10 data scientists already.

Another observation is that in 2018, barely 2 percent of companies had more than 1,000 data scientists; today that number is just over 3 percent, indicating small but significant growth. These companies include the big FAANG tech giants—Facebook, Apple, Amazon, Netflix, and Google (Yahoo); their large data science teams are working to maintain competitiveness as more third-party solutions crop up.

我们今年收集的数据之一是被调查者工作地点雇佣的数据科学家的数量。在我们经常与公司的交谈中，我们反复听到管理层将数据科学职位的招聘优先于其他许多职位，而不是其他许多人，包括工程、IT和软件。以下是调查结果。

在接受调查的两组人中，有一半人表示，他们的公司雇佣1至10名数据科学家。这实际上比2018年有所下降，当时有58%的公司声称雇佣了1至10名数据科学家。

由于对人工智能和ML的投资正在增加，预计这一数字还会增加(Gartner)。然而，在全面数据的背景下进行评估，就会发现下降趋势的一个可能原因。2018年，18%的公司雇佣了11名或以上的数据科学家。然而，今年这一数字飙升至39%，这表明，在所有行业，企业都在加大招聘力度，以建立更大的数据科学武库，其中一些企业已经从近10名数据科学家开始招聘。

另一个观察结果是，2018年，只有2%的公司拥有超过1000名数据科学家;如今，这个数字仅略高于3%，这表明增长虽小但意义重大。这些公司包括FAANG科技巨头——Facebook、苹果(Apple)、亚马逊(Amazon)、Netflix和谷歌(Yahoo);随着越来越多的第三方解决方案涌现，它们的大型数据科学团队正在努力保持竞争力。

Reflects data from both survey groups. Note that respondents who did not know or were unsure are not depicted in this graph.

反映了两个调查群体的数据。请注意，不知道或不确定的受访者没有在这张图表中描述。

In 2016, Deloitte predicted a shortage of 180,000 data scientists by 2018, and between 2012 and 2017, the number of data scientist jobs on LinkedIn increased by more than 650 percent (KDnuggets). The talent deficit and high demand means that hiring and maintaining data science roles will only become more difficult for small and mid-sized companies that cannot offer the same salary and benefits packages as the FAANG companies.

As demand for data scientists grows, we may see a trend of junior-level hires having less opportunity to structure data science and machine learning efforts within their teams, as much of the structuring and program scoping may have already been done by predecessors who overcame the initial hurdles. It could also mean, however, that leadership alignment has already been attained so ML teams will have more ownership and leeway in project execution.

2016 年，德勤预测到 2018 年将短缺 180,000 名数据科学家，而在 2012 年至 2017 年期间，LinkedIn 上的数据科学家职位数量增加了 650% 以上（KDnuggets）。 人才短缺和高需求意味着，对于无法提供与 FAANG 公司相同的薪资和福利待遇的中小型公司而言，招聘和维护数据科学职位只会变得更加困难。

随着对数据科学家的需求增长，我们可能会看到初级员工在其团队中构建数据科学和机器学习工作的机会减少的趋势，因为许多结构化和程序范围划分可能已经由克服最初障碍的前辈完成。 然而，这也可能意味着领导层已经达成一致，因此 ML 团队将在项目执行中拥有更多的所有权和回旋余地。

Finally, we may also see the merging of traditional business intelligence and data science in order to fill immediate requirements in the latter talent pool since both domains use data modeling (BI work uses statistical methods to analyze past performance, and data science makes predictions about future events or performance).

Gartner predicts that the overall lack of data science resources will result in an increasing number of developers becoming involved in creating and managing machine learning models (Gartner CIO survey). This blending of roles, will likely lead to another phenomenon related to this finding: more role names and job titles for the same sorts of work. To that end, we are seeing an influx of new job titles in data science such as Machine Learning Engineer, ML Developer, ML Architect, Data Engineer, Machine Learning Operations (ML Ops), and AI Ops as the industry expands and companies attempt to distinguish themselves and their talent from the pack.

最后，我们可能还会看到传统商业智能和数据科学的融合，以满足后者人才库的即时需求，因为这两个领域都使用数据建模（BI工作使用统计方法分析过去的绩效，数据科学对未来事件或绩效进行预测）。

Gartner 预测，数据科学资源的整体缺乏将导致越来越多的开发人员参与创建和管理机器学习模型（Gartner CIO 调查）。 这种角色的混合，很可能会导致与这一发现相关的另一种现象：相同类型的工作有更多的角色名称和职务。 随着行业的扩张，公司试图将自己和他们的人才从群体中脱颖而出，数据科学领域的新职位如机器学习工程师、ML开发人员、ML架构师、数据工程师、机器学习运营（ML Ops）和AI Ops大量涌现。

As a company, we are interested in machine learning applications in the enterprise and we strive to keep

a pulse on how industries are using emerging ML tech to automate workflows. There are countless ways to apply ML to a particular business problem, such as using prediction modeling to make assessments about customer churn or applying natural language processing to millions of tweets to analyze the percentage of negative sentiments.

In this year’s survey, we polled respondents about the ways their companies are using machine learning to ensure our understanding of the landscape is accurate or that we aren’t missing a key use case entering the enterprise. We anticipated a trend toward using ML to automate time-consuming processes and cut down on the number of human resources needed to do a given task. The results are depicted below.

作为一家公司，我们对机器学习在企业中的应用感兴趣，并努力保持

行业如何利用新兴的ML技术实现工作流自动化。有无数种方法可以将ML应用于特定的业务问题，比如使用预测建模来评估客户流失，或者对数百万条tweet应用自然语言处理来分析负面情绪的百分比。

在今年的调查中，我们对受访者进行了调查，询问他们的公司如何使用机器学习来确保我们对环境的理解是准确的，或者我们是否遗漏了进入企业的关键用例。我们预计会有一种趋势，即使用ML自动化耗时的流程，并减少执行给定任务所需的人力资源数量。结果如下所示。

Reflects data only from survey Group B. Note that respondents were allowed to choose more than one answer.

仅反映调查组B的数据。注意，受访者被允许选择一个以上的答案。

In this year’s survey, we provided a wide-ranging list of possible use cases and a write-in option. Respondents were encouraged to select all answers that applied to how their companies use AI and ML models today. The top three machine learning use cases across the board (for companies of all sizes) were as follows:

(1)、Reducing company costs

(2)、Generating customer insights and intelligence

(3)、Improving customer experience

在今年的调查中，我们提供了一个范围广泛的可能用例列表和一个写入选项。鼓励受访者选择适用于他们的公司今天如何使用 AI 和 ML 模型的所有答案。全面排名前三的机器学习用例（适用于各种规模的公司）如下：

(1)、降低公司成本

(2)、生成客户洞察和情报

(3)、提升客户体验

When we break down the data by company size, we start to see some differentiation in priorities. The top five ML use cases for companies with 10,000 employees or more:

(1)、Reducing company costs

(2)、Process automation for internal organization

(3)、Improving customer experience

(4)、Generating customer insights and intelligence

(5)、Detecting fraud

当我们按公司规模细分数据时，我们开始看到优先级有所不同。拥有 10,000 名或更多员工的公司的前五个 ML 用例：

(1)、降低公司成本

(2)、内部组织过程自动化

(3)、提升客户体验

(4)、生成客户洞察和情报

(5)、检测欺诈

The top five ML uses cases for companies with 1,001-5,000 employees:

(1)、Reducing company costs

(2)、Retaining customers

(3)、Process automation for internal organization

(4)、Recommender systems

(5)、Increasing customer satisfaction

拥有 1,001-5,000 名员工的公司的前五个 ML 用例：

(1)、降低公司成本

(2)、留住客户

(3)、内部组织过程自动化

(4)、推荐系统

(5)、提高客户满意度

The top five ML use cases for companies with fewer than 100 employees:

(1)、Generating customer insights and intelligence

(2)、Improving customer experience

(3)、Reducing company costs

(4)、Increasing customer satisfaction

(5)、Retaining customers

员工人数少于 100 人的公司的前 5 个 ML 用例：

(1)、生成客户洞察和情报

(2)、提升客户体验

(3)、降低公司成本

(4)、提高客户满意度

(5)、留住客户

The survey data showed that large companies are using ML primarily for internal applications (reducing company spend and automating internal processes), and smaller companies are primarily focused on customer-centric functions (increasing customer satisfaction, improving customer experience, and gathering insights). This suggests that as companies grow, they prioritize customer service less than cost-saving measures and applications that improve their product lines. Doing so comes at a price, however, as one-third of Americans consider switching companies after just one instance of poor customer service (Qualtrics). Conversely, an increase in customer retention rate of just 5 percent can produce more than a 25-percent increase in profits (Bain&Company).

调查数据显示，大公司主要将 ML 用于内部应用程序（减少公司支出和自动化内部流程），而小公司主要关注以客户为中心的功能（提高客户满意度、改善客户体验和收集见解）。这表明，随着公司的发展，他们优先考虑客户服务，而不是节省成本的措施和改善其产品线的应用程序。然而，这样做是有代价的，因为三分之一的美国人在一次糟糕的客户服务（Qualtrics）之后考虑更换公司。相反，客户保留率仅增加 5% 就能产生超过 25% 的利润增长（Bain&Company）。

Fortunately, machine learning is a solution for both types of business problems—cutting costs and customer satisfaction—and will likely shift business priorities in the near term as workflows are drastically augmented by new tech. For comparison, in our 2018 survey, 48 percent of respondents from companies with 10,000 or more employees said cost savings was a major ML priority, and 59 percent said increasing customer loyalty was the top ML use case, depicting a notable shift away from customers this year. It will be important to monitor this metric in future years to see if this is the beginning of a trend or an anomaly.

Before conducting this year’s survey, we anticipated a more even spread of use cases across companies of all sizes independent of industry because of the number of companies and applications in development in the AI/ML space (Forbes). The percentages for cost reduction, roboticprocessautomation, and customer service applications may be an indicator of ML’s general newness and immaturity, which our next key finding discusses, or it may be demonstrative of the fact that those types of repetitive applications lend themselves more readily to automation. As machine learning becomes more sophisticated with time, we are likely to see a wider pool of use cases designed for specific organizational initiatives.

幸运的是，机器学习是解决这两种业务问题（削减成本和客户满意度）的解决方案，并且可能会在短期内改变业务重点，因为新技术极大地增强了工作流程。相比之下，在我们 2018 年的调查中，来自拥有 10,000 名或更多员工的公司的 48% 的受访者表示，节省成本是 ML 的主要优先事项，59% 的受访者表示，提高客户忠诚度是 ML 的首要用例，这这表明今年的ML用户明显减少。在未来几年监控这一指标以查看这是趋势的开始还是异常情况非常重要。

在进行今年的调查之前，由于 AI/ML 领域（福布斯）中正在开发的公司和应用程序的数量，我们预计使用案例在各种规模的公司中的分布会更加均匀，而与行业无关。成本降低、机器人流程自动化和客户服务应用程序的百分比可能是 ML 普遍新颖和不成熟的指标，我们的下一个关键发现将讨论这一点，或者它可能表明这些类型的重复应用程序更容易实现自动化这一事实.随着机器学习随着时间的推移变得越来越复杂，我们可能会看到为特定组织计划设计的更广泛的用例池。

Understandably, industries with customer-facing products or services (retail, manufacturing, healthcare, etc.) prioritize ML applications that improve customer service, and industries involved with security, compliance laws, and proprietary data (financial institutions, government agencies, insurers, etc.) focus more so on ML use cases that help solve those challenges. The following are a few noteworthy examples:

可以理解，拥有面向客户的产品或服务（零售、制造、医疗保健等）的行业优先考虑改善客户服务的 ML 应用程序，以及涉及安全、合规法律和专有数据的行业（金融机构、政府机构、保险公司等） .) 更多地关注有助于解决这些挑战的机器学习用例。以下是一些值得注意的例子：

Respondents in both survey groups who work in consulting and professional services industries said that reducing customer churn was their top ML priority.

The education/edtech sector’s top ML use case was interacting with customers, which is reasonable considering that students and instructors are likely a primary customer set in those industries.

For the healthcare, pharmaceutical, and biotech industries, increasing customer satisfaction was the leading use case, suggesting that customer dissatisfaction or churn may be a continual challenge in those fields.

IT companies use ML primarily to acquire new customers, and software development organizations prioritize ML recommendersystems to guide users toward viewing new products or features to buy.

Banks and financial services firms are focusing their ML efforts on retainingcustomers and detecting fraud—keeping customers happy and mitigating vulnerabilities to the company.

Finally, the energy sector, including utility companies, are focusing on forecasting demand fluctuations using ML, likely to prevent power outages, reduce response times during disruptions of service, and plan for power consumption for coming years (NeuralDesigner).

在咨询和专业服务行业工作的两个调查组中的受访者都表示，减少客户流失是他们在机器学习方面的首要任务。

教育/edtech 行业的顶级 ML 用例是与客户交互，考虑到学生和教师可能是这些行业的主要客户群，这是合理的。

对于医疗保健、制药和生物技术行业，提高客户满意度是主要用例，这表明客户不满意或流失可能是这些领域的持续挑战。

IT 公司主要使用 ML 来获取新客户，软件开发组织优先使用 ML 推荐系统来引导用户查看新产品或购买新功能。

银行和金融服务公司正在将他们的机器学习工作重点放在留住客户和检测欺诈上——让客户满意并减少公司的漏洞。

最后，包括公用事业公司在内的能源部门正专注于使用 ML 预测需求波动，可能防止停电，缩短服务中断期间的响应时间，并规划未来几年的用电量（NeuralDesigner）。

Understanding how companies view their own machine learning maturity provides insight into future developments in the ML space. For this survey, we asked respondents to gauge where they think their companies are located currently on the machinelearningroadmap. That is to say, we sought to determine if they are just starting to consider machine learning applications for business problems or if they are operating a fully developed machine learning program, or somewhere in the middle of that spectrum, and whether their positioning has changed in the previous 12 months.

In 2018’s survey report, nearly 40 percent of respondents said they were just beginning to develop ML plans (ie. evaluating use cases, starting to build models). Moreover, in 2018 fewer than 10 percent of respondents considered themselves at a sophisticated ML maturity level.

了解公司如何看待自己的机器学习成熟度可以洞察机器学习领域的未来发展。在本次调查中，我们要求受访者评估他们认为他们的公司目前在机器学习路线图上的位置。也就是说，我们试图确定他们是否刚刚开始考虑将机器学习应用程序用于解决业务问题，或者他们是否正在运行一个完全开发的机器学习程序，或者处于该范围的中间，以及他们的定位是否在过去的12个月改变。

在 2018 年的调查报告中，近 40% 的受访者表示他们刚刚开始制定 ML 计划（即评估用例，开始构建模型）。此外，在 2018 年，不到 10% 的受访者认为自己处于复杂的 ML 成熟度水平。

This year, we asked respondents to select one of the following options to gauge ML maturity levels:

Not actively considering ML as a business solution

Evaluating ML use cases

Just starting to develop/build models

Developed models; working toward production

Early stage adoption (models in production for 1-2 years)

Mid-stage adoption (models in production for 2-4 years)

Sophisticated (models in production for 5+ years)

今年，我们要求受访者选择以下选项之一来衡量 ML 成熟度水平：

没有积极考虑将 ML 作为业务解决方案

评估机器学习用例

刚开始开发/构建模型

开发模型；致力于生产

早期采用（模型生产 1-2 年）

中期采用（模型生产 2-4 年）

复杂（模型生产 5 年以上）

Of the respondents who are actively engaging in ML (removing the first category of those who are not evaluating ML as a business solution), about one-fifth said they are evaluating use cases, based on an average of both survey groups. Those just starting to develop and build models numbered 17 percent, and a separate 17 percent of companies have developed models but are still working toward production. This means that 55 percent of companies surveyed have not deployed a machine learning model.

根据两个调查组的平均值，在积极参与 ML 的受访者中（删除了不将 ML 作为业务解决方案评估的第一类），大约五分之一的人表示他们正在评估用例。 刚开始开发和建造模型的公司占 17%，另外 17% 的公司已经开发了模型，但仍在努力生产。 这意味着 55% 的受访公司尚未部署机器学习模型。

ML in early stages of development

The number of companies with undeployed models is up 4 percent from last year, likely because there are more companies across the board beginning ML journeys, inflating the category of newcomers. It is important to note as well that our survey sample increased by more than 200 people from last year.

处于早期开发阶段的机器学习

未部署模型的公司数量比去年增加了 4%，这可能是因为有更多公司全面开始 ML 之旅，从而扩大了新人的类别。 还需要注意的是，我们的调查样本比去年增加了 200 多人。

Just over 22 percent of companies have had models in production for 1-2 years; last year, 13 percent of respondents claimed this, demonstrating a fairly significant migration toward productionization even if it is still early days for most companies

Moreover, one-fifth of companies said they plan on getting models into production within the next year, suggesting that we may see a noticeable portion of companies moving into the next maturity category (mid-stage) in the near term.

超过 22% 的公司已经生产了 1-2 年的模型；去年，有 13% 的受访者表示这一点，这表明即使对于大多数公司来说仍处于初期阶段，但向生产化的迁移也相当显着

此外，五分之一的公司表示他们计划在明年将模型投入生产，这表明我们可能会看到相当一部分公司在短期内进入下一个成熟类别（中期）。

In 2018, only 6 percent of respondents considered their companies to have sophisticated ML programs. This year, 8 percent do, and the majority of companies in the sophisticated category either have fewer than 500 employees or more than 10,000. In 2018, 39 percent of sophisticated companies had fewer than 100 employees and 29 percent had more than 10,000 employees.

There are several ways to read this maturity breakdown. First, large companies typically have more budget for innovation hubs and emerging technology, thus streamlining the development of sophisticated ML initiatives. Smaller companies, however, can be quite agile technologically, able to build, buy, and iterate quickly.

2018 年，只有 6% 的受访者认为他们的公司拥有复杂的 ML 程序。今年，这一比例为 8%，大多数复杂类别的公司员工人数少于 500 人或超过 10,000 人。 2018 年，39% 的成熟公司员工人数少于 100 人，29% 的公司员工人数超过 10,000 人。

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