面对AI（人工智能） ML（机器学习） data science（数据科学） 不再傻傻分不清

  • Artificial intelligence (AI) is hyped: Hype about AI is at its peak, but AI must be distinguished from data science and ML. Of course, data science is a core discipline for the development of AI, and ML is a core enabler of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions. AI is on a different level of aggregation to data science and ML. AI is at the application level. Data science and ML models must be combined to work together with other capabilities, such as a UI and workflow management, to constitute an AI application. A self-driving car, for example, has ML capability, but its AI requires much more than that.
  • 人工智能（AI）被大肆宣传：关于AI的炒作正处于巅峰期，但AI必须与数据科学和ML区分开来。当然，数据科学是人工智能开发的核心学科，而ML是人工智能的核心推动者，但这不是全部。ML是关于创建和训练模型; AI就是在某些条件下使用这些模型来推断结论。AI与数据科学和ML的聚合程度不同。AI处于应用程序级别。必须将数据科学和ML模型结合起来与其他功能（例如UI和工作流管理）一起构成AI应用程序。例如，自动驾驶汽车具有ML功能，但其AI要求远不止于此。

The diversity of data science and ML platforms largely reflects the wide range of people that use them. A variety of audiences:

数据科学和ML平台的多样性在很大程度上反映了使用它们的广泛人群。例如各种受众：

  • Citizen data scientists: Increasingly, these are accessing data and building data science and ML models. They are people who need access to data science and ML capabilities, but who do not have the advanced skills of traditional expert data scientists. Citizen data scientists can come from roles such as business analyst, line of business (LOB) analyst, data engineer and application developer. They need to understand the nature of the data science and ML market, and how it differs from, but complements, the analytics and business intelligence (BI) market (see “Magic Quadrant for Analytics and Business Intelligence Platforms”). Citizen data scientists do not replace expert data scientists but, instead, work in collaboration with them.
  • 公民数据科学家：越来越多的人正在访问数据，建立数据科学和ML模型。他们是需要获得数据科学和ML能力的人，但他们没有传统专家数据科学家的高级技能。公民数据科学家可以来自业务分析师，业务线（LOB）分析师，数据工程师和应用程序开发人员等角色。他们需要了解数据科学和ML市场的性质，以及它与分析和商业智能（BI）市场的不同之处，而不是补充，（请参阅 “分析和商业智能平台的魔力象限” ）。公民数据科学家不会取代专家数据科学家，而是与他们合作。
  • Line of business (LOB) data science teams: Typically, these are sponsored by their LOB’s executive and charged with addressing LOB-led initiatives in areas such as marketing, risk management and CRM. They focus on their own and their department’s priorities. Levels of collaboration with other LOB data science teams vary. LOB data science teams can include both expert and citizen data scientists.
  • 业务线（LOB）数据科学团队：通常，这些团队由其LOB执行官赞助，负责解决LOB主导的营销，风险管理和CRM等领域的计划。他们专注于自己和部门的优先事项。与其他LOB数据科学团队的合作级别各不相同。LOB数据科学团队可以包括专家和公民数据科学家。
  • Corporate data science teams: These have strong and broad executive sponsorship, and can take a cross-functional perspective from a position of enterprisewide visibility. In addition to supporting model building, they are often charged with defining and supporting an end-to-end process for building and deploying data science and ML models. They often work in partnership with LOB data science teams in multitier organizations. In addition, they might provide LOB assistance for LOB teams that do not have their own data scientists. Corporate data science teams typically include expert data scientists.
  • 企业数据科学团队：这些团队拥有强大而广泛的高管赞助，并且可以从企业范围的可见性角度采取跨职能视角。除了支持模型构建之外，它们还经常负责定义和支持用于构建和部署数据科学和ML模型的端到端流程。他们经常与多层组织中的LOB数据科学团队合作。此外，他们可能会为没有自己的数据科学家的LOB团队提供LOB援助。公司数据科学团队通常包括专家数据科学家。
  • “Maverick” data scientists: These are typically one-off scientists in various LOBs. They tend to work independently on “point” solutions and usually strongly favor open-source tools, such as Python, R and Apache Spark. They rarely collaborate much with other data scientists or departments within their organization.
  • “Maverick”数据科学家：这些通常是各种LOB中的一次性科学家。他们倾向于在“点”解决方案上独立工作，并且通常非常支持开源工具，例如Python，R和Apache Spark。他们很少与其组织内的其他数据科学家或部门合作。