MBA教育中心知识库

　　随着国家将大数据、人工智能纳入新基建重点发展方向，越来越多的企业希望通过人工智能充分理解自己海量私有数据，提高企业快速决策效率。机器学习云平台基于云计算基础设施，帮助数据科学家、机器学习工程师和企业决策者创建、部署、管理机器学习模型产生商业洞见。
　　SageMaker是基于亚马逊云的机器学习平台产品，因其丰富的行业覆盖以及技术前瞻性在世界占据统治地位。SageMaker于2020年四月引入中国，依赖其海外影响力以及众多国际大客户成功故事，本应迅速占领中国市场，然而经过三年的发展，其份额仍在五名以外。本研究以SageMaker在中国运营为研究对象，通过深入分析其运营策略提出针对性的优化方案。
　　本研究以漏斗逻辑，对云计算、机器学习云平台、运营管理的概念进行界定，并对机器学习云平台国内外研究现状进行系统梳理。在明确相关概念和理论基础后，作者展开了对SageMaker在中国运营的环境分析。作者首先利用PEST宏观环境分析法对其从政治、经济、社会、技术角度加以研究。然后从技术、价格、品牌、生态链四个方面结合对高层的访谈进行运营状况的微观环境分析。最后利用SWOT分析法综合宏观和微观分析，发现SageMaker在中国运营可以利用的优势、机会以及要克服的劣势、挑战，继而完成了反映其在中国运营策略的波士顿矩阵。在SageMaker运营问题的研究中，作者运用访谈法整理出运营存在的主要问题，又分别回归到技术、价格、品牌、生态链四个方面，概括成如下四个核心问题。第一，缺失关键技术功能，导致无法完成平台闭环的技术运营问题。第二，对客户总体拥有成本不友好的价格运营问题。第三，无法大量复制成功案例，做到快速一到一百市场传播的品牌运营问题；第四，生态链发展滞后、存在明显短板的生态链运营问题。在最后的运营策略优化中，作者以SWOT分析为基础,以运营管理中“精益生产理论”和“比较优势理论”作为优化依据，综合利用头脑风暴法、鱼骨图法对四个核心运营问题提出优化建议。

　　With the proposal of China's new infrastructure initiative, big data and artificial intelligence have been listed as priority strategic directions. Turning data into business insights through machine learning has become a key development area. Machine learning cloud platforms are an important component aligned with the development of this field. Machine learning cloud platforms, based on cloud computing infrastructure, assist data scientists, machine learning engineers, and software developers in creating, deploying, and managing machine learning models. Machine learning cloud platforms provide a unified development environment, enabling more efficient collaboration across departments and disciplines. Moreover, as machine learning cloud platforms can effectively manage massive amounts of data and accelerate model development by leveraging abundant computational resources, they are more suitable for large and medium-sized enterprise customers.
　　SageMaker, based on Amazon Web Services, dominates the world in machine learning platforms owing to its extensive industry coverage and technical leadership. Introduced to China in April 2020, SageMaker was expected to swiftly grasp the China market relying on its overseas influence and international customer success stories. However, after three years of development, its market share is outside the top five. This research takes SageMaker's operations in China as the object of study. By thoroughly analyzing its operational strategy, this research aims to assist SageMaker in better localizing for China. It also hopes to provide references for domestic machine learning cloud platform vendors expanding overseas markets.
　　Following a funnel logic, this research explains the concepts of cloud computing and machine learning cloud platforms. It also systematically combs the research status of machine learning cloud platforms both domestically and abroad. By introducing lean production theory and comparative advantage theory in operational management, this research provides theoretical basis for researching the operations of SageMaker. Subsequently, the PEST analysis method is utilized to analyze the domestic environment for SageMaker. By drawing a Boston Matrix through SWOT analysis, SageMaker's strengths, opportunities and weaknesses & challenges in China are identified from the perspectives of technological operations, pricing operations, brand operations, and ecosystem operations. By comprehensively applying research methods including expert judgement, brainstorming, and fishbone diagram, the following problems are optimized one by one. Firstly, the lack of key technological functions leads to the inability to complete a closed platform loop. Secondly, it is not cost-friendly regarding customers' total cost of ownership. Thirdly, successful cases cannot be massively replicated to achieve exponential growth in market penetration. Fourthly, the partner ecosystem is inadequate. Finally, concrete operational optimization suggestions are proposed.