Alibaba, an entity known globally in the digital realm, has been taking steps to broaden its influence by actions like the Alibaba Cloud initiative. Through a complex system involving online commerce, financial technology, and cloud services, Alibaba has established itself as a significant presence in the digital alteration arena. By leveraging its technological skills and substantial resources, Alibaba has entered artificial intelligence (AI) with the establishment of the Platform for AI (PAI). PAI functions as an integrated platform facilitating an assortment of machine learning algorithms, well-known frameworks, and visualization tools, rendering it a flexible and robust solution for AI advancement. Additionally, Alibaba's external approach, noticeable in its undertakings in the Asia-Pacific area and other regions, underscores the company's aspirations to broaden its impact globally via digital technologies. This ambition corresponds with the overarching concept of ecosystem synergies and sustainable expansion recognized in the analysis of Alibaba's ecosystem dynamics.
Alibaba Group's strategic stance in the sphere of artificial intelligence (AI) and cloud computing is highlighted by its notable expansion and market value growth, indicating the company's significant influence on the AI technology landscape. The eminence of machine vision as a pivotal AI application is delineated in (de Seta et al., 2023), showcasing Alibaba's pioneering endeavours in capitalizing on sophisticated technologies like facial recognition and object tracking. The company's dedication to advancing AI capabilities is evident in its investment in the development of the Platform for AI (PAI), as discussed in (Yating et al., 2020). PAI's broad support for various machine learning algorithms and frameworks epitomizes Alibaba's comprehensive strategy in facilitating the widespread use of AI tools and promoting accessibility to state-of-the-art modelling techniques. By navigating the complex intersections of AI, cloud computing, and data analytics, Alibaba's cohesive initiatives align with overarching industry tendencies aimed at optimizing computational efficiencies and fostering technological progress.
The amalgamation of Artificial Intelligence (AI) in the realm of Cloud Computing bears substantial ramifications for the progression and implementation of cutting-edge technologies. Through the infusion of AI, cloud-based platforms can elevate their efficacy by facilitating intricate tasks, including but not limited to data scrutiny, machine learning, and pattern identification, in a highly proficient manner. The adeptness of AI in streamlining operations and furnishing astute perceptions could ultimately culminate in heightened resource exploitation, financial economies, and augmented efficiency within cloud-based ecosystems. An illustrative example can be delineated through the provision of a comprehensive array of tools and algorithms encapsulated within Alibaba Cloud's Platform for AI (PAI), which streamlines the process of constructing and deploying machine learning models for end users. Encompassing the capabilities of AI in the domain of Cloud Computing enables enterprises to propel innovation and propel digital metamorphosis by capitalizing on the immense capacity of copious datasets. This communalistic relationship between AI and Cloud Computing underscores their synergistic proclivity in propelling the frontiers of technological progress [ExtractedKnowledge1].
The emergence and widespread adoption of Machine Learning (ML) technologies have triggered an increased demand for ML hardware and software systems, leading to a notable proliferation of organizations involved in the development of ML inference chips. This rapid growth has resulted in a diverse range of ML inference systems with varying power consumption and performance metrics, accentuating the necessity for a robust benchmarking methodology to ensure meaningful comparisons across different architectures. The MLPerf Inference initiative, a collaborative endeavour encompassing numerous organizations and industry experts, presents a standardized framework for evaluating the performance of ML inference systems. The benchmark's versatility and adaptability were evident in the significant industry response, culminating in the submission of over 600 replicable inference-performance data points across 30 distinct systems. This industry-wide standardization endeavour plays a vital role in tackling the intricate and diverse landscape of assessing ML-system performance (Anderson et al., 2020). Concurrently, the evolution of ecosystems, as demonstrated through a longitudinal analysis of the Alibaba ecosystem, provides valuable insights into the dynamics of synergies, transformations, and coordination within complex organizational settings. The utilization of a case study approach in examining the Alibaba ecosystem spanning from 1999 to 2020 unveiled a multi-faceted understanding of ecosystem synergies, emphasizing the integration of resources to enhance efficiency, facilitation of transformative changes for evolution, and management of conflicts to support sustainable expansion. This refined conceptual framework enriches comprehension regarding the distinctive synergistic dynamics inherent in ecosystems and sheds light on the motivating factors driving collaborative engagement for value creation. Moreover, the discourse on ecosystem evolution elucidates a dualistic interplay between intentionality and emergence, challenging existing binary viewpoints and proposing a phased model for sustainable development propelled by both internal and external influences. The concept of ecosystem orchestration emerges as a pivotal factor, highlighting the systematic alignment of technological, adoption, internal, and institutional initiatives guided by long-term visions and fine-tuned through iterative revisions (Cao et al., 2023). By synthesizing these scholarly dialogues on ecosystem dynamics and ML benchmarking, a holistic insight into the Alibaba Cloud Platform for AI materializes, emphasizing the significance of standardized evaluation criteria in navigating the intricate landscape of ML technologies within interconnected ecosystems.
The examination of the functionalities of the Alibaba Cloud Platform for Artificial Intelligence reveals the pivotal role played by the Platform for AI (PAI) as an all-encompassing resource for initiatives involving machine learning. The inclusivity of the platform towards a myriad of machine learning algorithms, popular frameworks, and methods for visual modelling serves as a testament to its flexibility and efficiency in meeting the diverse demands of both research and business spheres. The amalgamation of such a vast spectrum of resources not only enriches the practicality of the platform but also signifies a staunch dedication towards nurturing innovation and effectiveness within the domain of Artificial Intelligence applications. As exemplified in a scholarly source (Bhagat Smriti et al., 2019), the platform's adeptness in managing extensive networks comprising a variety of nodes and edges effectively showcases its scalability and resilience in dealing with intricate data structures. Furthermore, as expounded in another reputable publication (Włoch et al., 2022), the platform's purpose in navigating through the complexities of the digital economy and discerning the ramifications of technological disruptions is in alignment with the broader narrative of the revolutionary capacity harboured by Artificial Intelligence. By harnessing the potentials encapsulated within the frameworks, the Alibaba Cloud Platform for Artificial Intelligence emerges as a versatile and futuristic instrument in propelling advancements within the realms of machine learning research and real-world applications.
The literature review (Chen et al., 2023) extensively discusses serverless computing, highlighting the importance of mainstream machine learning framework compatibility in the technological realm. Given the rapid evolution of artificial intelligence, seamless integration with established machine learning frameworks is crucial for the effectiveness and efficiency of AI platforms like Alibaba Cloud's Platform for AI (PAI). Through addressing serverless computing challenges with innovative solutions and research endeavours, as indicated in the research findings (Chen et al., 2023), PAI can capitalize on the latest advancements in machine learning frameworks to bolster its capabilities. This focus on mainstream framework compatibility not only boosts the platform's flexibility but also underscores its dedication to facilitating sophisticated AI applications that cater to users' dynamic requirements. The convergence of serverless computing research trends and the use of mainstream machine learning frameworks within PAI sheds light on a pivotal aspect of technological progress in AI platforms, stressing the significance of adaptability and integration in advancing machine learning applications.
The integration of visual modelling techniques in the Alibaba Cloud Platform for AI is crucial for improving the efficiency and interpretability of machine learning procedures. Research by (Gu et al., 2023) emphasizes the necessity of interpretable models in accurately forecasting electric load within the electric power sector, particularly when critical decisions need to be made. By employing interactive GAMs infused with domain expertise, the platform can achieve better performance and enhance its ability to generalize, thus dealing with the challenges posed by extreme weather conditions and data constraints. Moreover, a study by (Brännström et al., 2021) accentuates the significance of digital transformation for companies like Nudie Jeans Co., which relies on advanced analytics and predictive modelling to maintain competitiveness in the fast-paced fashion retail domain. Consequently, by integrating visual modelling approaches in the Alibaba Cloud Platform for AI, users can gain valuable insights for decision-making, process optimization, and fostering innovation across various industrial sectors, thereby supporting the platform's overarching goal of facilitating comprehensive machine learning functionalities.
The examination of the amalgamation capacities of Alibaba Cloud amenities within the milieu of the Alibaba Cloud Platform for Artificial Intelligence (PAI) necessitates contemplation of the ecosystem harmonies and the potential for orchestration underscored in scholarly works. The conceptual framework of ecosystem harmonies, as explicated in a pertinent source (Cao et al., 2023), accentuates the necessity to arrange and fuse resources proficiently, enable transformative shifts, and manage conflicts for enduring progress. This model can guide the development and execution of integrative components within PAI, guaranteeing smooth interchange with alternative Alibaba Cloud amenities. Furthermore, the investigation into telemedicine resolutions in a scholarly article (chen nuoya, 2021) underscores the significance of comprehending stakeholder requisites and facilitating the assimilation of technological resolutions with institutional stakeholders. The application of these perceptions to the evolution of PAI can augment its capacities to merge with a diverse array of Alibaba Cloud amenities proficiently, thereby refining its functionality and worth for users in the sphere of artificial intelligence and machine learning.
Amidst the complexities of the contemporary global economic milieu shaped by the realms of digital marketing and electronic commerce, the retail sector assumes a pivotal role in spearheading innovative paradigms and adaptive measures. The exigencies brought to the fore by the COVID-19 pandemic have accentuated the criticality of fortifying supply chains and embracing digital transformations to combat disruptions, as expounded in the elucidation proffered by (Gu et al., 2022). This scholarly work sheds light on how prominent e-commerce entities such as Alibaba can exploit technological advancements, exemplified by the Alibaba Cloud Platform for Artificial Intelligence, to optimize supply chain governance and cater to the changing preferences of consumers. Furthermore, the discourse on the revolution in digital marketing delineated by (Snezana Mojsovska Salamovska et al., 2019) accentuates the profound repercussions on consumer conduct in the retail domain, compelling enterprises to reassess their promotional strategies in the epoch of digitalization. The concept of omni-channel retailing emerges as a propitious remedy to interact with customers and synchronize with their escalated expectations in the dynamic e-commerce sphere. By amalgamating these insights, retailers can leverage the capabilities of AI-infused frameworks like Alibaba's Prediction Application Interface to negotiate the mutable retail ecosystem and furnish personalized clienteles experiences.
The amalgamation of digital technologies, exemplified by the Alibaba Cloud Platform for AI (PAI), exhibits substantial potential for revolutionizing the Healthcare and Life Sciences Sector through the mitigation of crucial obstacles and the enhancement of patient results. Drawing upon insights obtained from (chen nuoya, 2021), the amalgamation of telehealth solutions with organizational stakeholders and the receptiveness of elderly users to embrace such digital healthcare novelties in China highlight the possibility for a transformative impact. Additionally, as indicated in (Włoch et al., 2022), the paradigm shift in the digital economy necessitates a reassessment of conventional healthcare business models in view of intelligent automation and global tendencies. Through the utilization of PAI's capabilities in machine learning algorithms and visualized modelling techniques, the sector can streamline operational procedures, enhance diagnostic precision, and expedite individualized treatment strategies. This intersection of technological progressions and empirical investigations establishes a sturdy groundwork for propelling healthcare provision forward and amplifying population health consequences in the epoch of digital evolution.
The intricate intertwining of financial services and Fintech applications within the framework of the highly advanced Alibaba Cloud Platform for AI yields a complex terrain merging technological facets with market intricacies (Harasim et al., 2022). Noteworthy is the impact of Fintech on capital markets, particularly in propelling the digitization movement from within the sector, underpinned by ground-breaking FinTech solutions that amplify efficacy and curtail expenses (Harasim et al., 2022). The reluctance of Digitech entities to delve into capital markets due to intricacies and feeble alignments with non-financial services is contrasted by the pivotal role of FinTech companies in revamping investment services and market frameworks. Moreover, the transformative influence of online market hubs, epitomized by Alibaba, on economic and societal sustainability accentuates the broader connotations of digital platforms in bridging global market realms and harmonizing opportunities with hazards. Grasping these confluences is imperative for policymakers and stakeholders seeking to steer through the swiftly evolving domain of financial services amidst the backdrop of cutting-edge AI platforms such as the Alibaba Cloud Platform for AI.
In the domain of smart urban areas and implementations of the Internet of Things (IoT), the amalgamation of cutting-edge technologies such as 5G, RFID, and touchless payment mechanisms have been acknowledged as pivotal components for augmenting intelligent parking resolutions. Studies carried out by the Smart Cities Research Center of Zhejiang Province accentuated the imperative to enhance end-user interaction via pioneering system frameworks (Altavilla et al., 2018). Moreover, the scrutiny of telemedicine resolutions in the domain of IoT uncovers substantial potential for confronting healthcare impediments within intelligent urban surroundings. The analysis of business blueprints, stakeholder fusion, and end-user approval in the telemedicine field not only accentuates the societal advantages of IoT applications but also underscores the significance of data clarity and compatibility for optimizing the medical value of digital health resolutions (chen nuoya, 2021). Through amalgamating technical advancements with healthcare innovations, smart urban schemes can refine efficiency, accessibility, and durability via the smooth amalgamation of IoT resolutions like the Alibaba Cloud Platform for Artificial Intelligence.
The examination of preceding investigations shows that the critical aspects of Alibaba's Cloud Platform for Artificial Intelligence (AI) (PAI) are its scalability and flexibility, which play a crucial role in influencing the framework of secure software development in serverless computing settings. The distinctive strategy introduced in (Abdelwahab et al., 2020) underlines the essential requirement for adaptable air propulsion devices capable of adjusting to various regions and surroundings, reflecting the necessity for expandable resolutions in the domain of IT infrastructure. Likewise, (Pusuluri et al., 2022) accentuates how serverless computing transforms the IT sphere by facilitating swift deployment of secure developmental contexts without the encumbrance of orthodox infrastructure issues. Concerning PAI, the advantages of scalability and flexibility result in the capability of the platform to effectively adjust to variegated machine learning algorithms and structures, aligning with the fluid character of AI applications. This fusion of scalability and flexibility not only amplifies the responsiveness of PAI but also fortifies its standing as an avant-garde instrument in the sphere of AI innovation.
Within the realm of the Alibaba Cloud Platform dedicated to Artificial Intelligence (AI), the critical examination of economic efficiency and the fine-tuning of resource allocation stand as pivotal undertakings in elevating the efficacy and output of AI-centric applications. By drawing insights from (Gill et al., 2022), which expounds upon the intricacies of adept scaling methodologies tailored for microservices within cloud-based computing infrastructures, the paramount nature of prognostic algorithms and versatile scaling strategies in attaining optimal resource deployment and operational efficacy is underscored. Moreover, as elucidated in (chen nuoya, 2021), the discussion on the requirements and plausible impediments in integrating telehealth remedies accentuates the imperative nature of substantiating the medical and health-oriented worth of contemporary digital health innovations. Through an amalgamation of these viewpoints, the fine-tuning of resource distribution and cost-efficient practices within the AI framework can be astutely harmonized with the imperative of catering to societal requisites and pioneering enhancements in the operational models. This amalgamation ultimately serves to upscale the collective competence and durability of the Alibaba Cloud Platform concerning AI functionalities.
The deployment of Alibaba's Platform for AI (PAI) presents noteworthy apprehensions pertaining to data security and confidentiality that require meticulous attention to guarantee the platform's soundness and user confidence. With the continuous transformation of the IT sector by cloud computing and serverless technologies (Pusuluri et al., 2022), the utilization of sophisticated platforms like PAI prompts inquiries regarding the safeguarding of confidential data and personal details. In the realm of telehealth solutions and IoT applications, the significance of data security escalates, particularly in the context of incorporating telehealth solutions with institutional stakeholders and the hurdles associated with fostering confidence in telehealth technologies (chen nuoya, 2021). The probability of data breaches, illicit entry, and privacy transgressions underscores the necessity for robust security protocols within the PAI framework. Tackling these issues encompasses not only technological defences but also the transparency of algorithms, data compatibility, and ethical contemplations in AI-based healthcare applications. Through prioritizing data security and privacy during the inception and implementation of PAI, Alibaba can establish itself as a reliable and accountable trailblazer in the AI domain.
Amidst the swift progression of e-commerce platforms such as Alibaba and the widespread utilization of Internet of Things (IoT) devices, the significance of performance and reliability in cloud infrastructures designated for Artificial Intelligence (AI) applications, such as Alibaba Cloud, emerges as a critical concern. The revolutionary influence of e-commerce platforms spotlights the intricate equilibrium between avenues for expansion and probable hazards, thereby accentuating the indispensability of steadfast performance and reliability within cloud frameworks. Furthermore, the scrutiny of IoT backends exemplifies the intricacy of operational methodologies and the interlinked nature of global IoT services, underscored the urgency of unwavering performance and reliability to endorse a variety of IoT traffic configurations. As cloud environments engineered for AI, such as Alibaba's Platform for AI (PAI), endeavour to cater to machine learning algorithms and infrastructures, guaranteeing optimal performance and reliability emerges as imperative to facilitate smooth data processing, modelling, and examination. By amalgamating understandings derived from e-commerce platforms and IoT ecosystems, cloud infrastructures can augment their competencies to fulfil the strenuous prerequisites of AI applications while upholding robust performance and reliability benchmarks, thereby refining the user experience and data processing efficiency.
In delving into the pivotal role that Alibaba Cloud plays in advancing AI technology, it becomes crucial to reflect on the intricate interdependencies within ecosystems and the significant impacts that digital platforms like Alibaba exert on economic, social, and environmental sustainability (Cao et al., 2023) (Baiocco et al., 2019). The Platform for AI (PAI) provided by Alibaba Cloud serves as a prime example of innovative solutions, presenting a holistic platform that caters to a variety of machine learning algorithms, prevalent frameworks, and visualized modelling techniques. This extensive framework highlights Alibaba's dedication to driving technological progress and uniting different global segments through accessible and effective AI solutions. As policymakers wrestle with the duties and sustainable strategies of digital platforms, Alibaba's proactive approach to fostering growth while managing risks showcases a forward-looking strategy in utilizing AI's potential for societal improvement. Through utilizing Alibaba Cloud's sturdy infrastructure and strategic outlook, the trajectory of AI technology appears primed for continuous advancement and societal influence.
[1]. Gu, Shiqi. (2022). The Impact of COVID-19 on Supply Chains and E-commerce Logistics. https://core.ac.uk/download/523299939.pdf
[2]. Snezana Mojsovska Salamovska, Yulia Syaglova. (2019). DIGITAL TRANSFORMATION IN MARKETING AND BUSINESS - IMPLICATIONS ON RETAIL TECHNOLOGIES AND CUSTOMER ENGAGEMENT. https://core.ac.uk/download/248080613.pdf
[3]. Harasim, Janina. (2022). Chapter 5 FinTechs, BigTechs and structural changes in capital markets. https://core.ac.uk/download/520256083.pdf
[4]. Baiocco, Sara, Echikson, William, Kilhoffer, Zachary, Lenaerts, Karolien, Li, Wenwei, Man, Luo, Shenglin, Ben, Simonelli, Felice, Zarra, Antonella. (2019). Sustainability in the Age of Platforms. CEPS Special Report. June 2019. https://core.ac.uk/download/237017648.pdf
[5]. Abdelwahab, Mohamed, Bohne, James, Choi, Nicholas M, Couvrette, Justin, Davis, Justin, Esmenjaud, Kobi, Fahmy, Daniel, Hernandez, Mario, Laxamana, Jacob, Murray, Jaelin Meiying, Orozco, Jacob, Rasmussen, Jacob, Singleton, Taajza, Symon, Jake T, Zhang, Scarlett Shiling. (2020). An Innovative Take on Filtering Carbon Dioxide Through CryoCapture. https://core.ac.uk/download/294625767.pdf
[6]. Pusuluri, Vasanta Swarna Ratnam. (2022). Taxonomy Of Security and Privacy Issues In Serverless Computing. https://core.ac.uk/download/543558514.pdf
[7]. Ravishankar, M.N., Zhang, Gongtao. (2018). Exploring vendor capabilities in the cloud environment: a case study of Alibaba Cloud Computing. https://core.ac.uk/download/162671833.pdf
[8]. Bootz, Valentin, Böttcher, Timo Phillip, Hein, Andreas, Schaffer, Norman, Weking, Jörg. (2022). Business Model Configurations for Digital Platform Success - Towards a Typology of Digital Platform Business Models. https://core.ac.uk/download/511301766.pdf
[9]. Gu, Xinyue, Ren, Rui, Sun, Liang, Yang, Linxiao. (2023). Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting. http://arxiv.org/abs/2310.15662
[10]. Brännström, Gustaf. (2021). Digital transformation of a Fashion Company - An internship and case study with focus on the implementation of enhanced Data Infrastructure & Business Intelligence based processes. https://core.ac.uk/download/481371951.pdf
[11]. Bhagat Smriti, Bojchevski Aleksandar, Hamilton Will, Huang Xiao, Kingma Diederik P, Lin Zhouhan, Mikolov Tomas, Mikolov Tomas, Tang Lei, Taskar Ben, Thomas, Yang Cheng, Yang Zhilin, Zhang Hongming. (2019). Representation Learning for Attributed Multiplex Heterogeneous Network. http://arxiv.org/abs/1905.01669
[12]. Włoch, Renata, Śledziewska, Katarzyna. (2022). Chapter 1 The foundations of the digital economy. https://core.ac.uk/download/520258693.pdf
[13]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf
[14]. Włoch, Renata, Śledziewska, Katarzyna. (2022). Chapter 3 How is production changing? https://core.ac.uk/download/520262218.pdf
[15]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[16]. Keane, Michael, Yu, Haiqing. (2019). A Digital Empire in the Making: China's Outbound Digital Platforms. https://core.ac.uk/download/328381530.pdf
[17]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[18]. Chen, Zhenpeng, Jin, Xin, Liu, Xuanzhe, Wen, Jinfeng. (2023). Rise of the Planet of Serverless Computing: A Systematic Review. https://core.ac.uk/download/552461257.pdf
[19]. Bhagat Smriti, Bojchevski Aleksandar, Hamilton Will, Huang Xiao, Kingma Diederik P, Lin Zhouhan, Mikolov Tomas, Mikolov Tomas, Tang Lei, Taskar Ben, Thomas, Yang Cheng, Yang Zhilin, Zhang Hongming. (2019). Representation Learning for Attributed Multiplex Heterogeneous Network. http://arxiv.org/abs/1905.01669
[20]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[21]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[22]. Baiocco, Sara, Echikson, William, Kilhoffer, Zachary, Lenaerts, Karolien, Li, Wenwei, Man, Luo, Shenglin, Ben, Simonelli, Felice, Zarra, Antonella. (2019). Sustainability in the Age of Platforms. CEPS Special Report. June 2019. https://core.ac.uk/download/237017648.pdf
[23]. Pusuluri, Vasanta Swarna Ratnam. (2022). TAXONOMY OF SECURITY AND PRIVACY ISSUES IN SERVERLESS COMPUTING. https://core.ac.uk/download/543558514.pdf
[24]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf.
[25]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[26]. Chen, Zhenpeng, Jin, Xin, Liu, Xuanzhe, Wen, Jinfeng. (2023). Rise of the Planet of Serverless Computing: A Systematic Review. https://core.ac.uk/download/552461257.pdf
[27]. Baiocco, Sara, Echikson, William, Kilhoffer, Zachary, Lenaerts, Karolien, Li, Wenwei, Man, Luo, Shenglin, Ben, Simonelli, Felice, Zarra, Antonella. (2019). Sustainability in the Age of Platforms. CEPS Special Report. June 2019. https://core.ac.uk/download/237017648.pdf
[28]. Feldmann, Anja, Gasser, Oliver, Matic, Srdjan, Saidi, Said Jawad, Smaragdakis, Georgios. (2022). Deep Dive into the IoT Backend Ecosystem. https://core.ac.uk/download/552340338.pdf
[29]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[30]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf
[31]. Sammut-Bonnici, Tanya. (2020). The sharing economy, jobs and skills. https://core.ac.uk/download/294836131.pdf
[32]. Baiocco, Sara, Echikson, William, Kilhoffer, Zachary, Lenaerts, Karolien, Li, Wenwei, Man, Luo, Shenglin, Ben, Simonelli, Felice, Zarra, Antonella. (2019). Sustainability in the Age of Platforms. CEPS Special Report. June 2019. https://core.ac.uk/download/237017648.pdf
[33]. de Seta, Gabriele, Shchetvina, Anya. (2023). Imagining machine vision: Four visual registers from the Chinese AI industry. https://core.ac.uk/download/588770005.pdf
[34]. Yating, Zhou. (2020). Alibaba –a chinese giant planning to take on the world of retail, cloud computing and others. https://core.ac.uk/download/421863320.pdf
[35]. Altavilla, Michael David, Contreras Albuja, Josue David, LeBeau, Evan R, Li, Shuxing. (2018). Developing Future Smart Parking Solutions for Hangzhouu27s IoT Town. https://core.ac.uk/download/212997379.pdf
[36]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf
[37]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[38]. Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Cheng, Christine, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St., Kanter, David, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Mattson, Peter, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Reddi, Vijay Janapa, Schmuelling, Guenther, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Carole-Jean, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhang, Peizhao, Zhong, Aaron, Zhou, Yuchen. (2020). MLPerf Inference Benchmark. http://arxiv.org/abs/1911.02549
[39]. Gill, SS, Ilager, S, Song, C, Xu, C, Xu, M, Ye, K, Zhao, J. (2022). CoScal: Multi-faceted Scaling of Microservices with Reinforcement Learning. https://core.ac.uk/download/541353875.pdf
[40]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf
[41]. Cao, Zhe (Selina). (2023). Ecosystem synergies, change and orchestration. https://core.ac.uk/download/588147796.pdf
[42]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf
[43]. chen nuoya. (2021). Smart Healthcare solutions in China and Europe, an international business perspective. https://core.ac.uk/download/491249162.pdf
[44]. Meccheri, Alessandro. (2014). China Goes Digital the Great Wall Culture and The Role of Search Engines. https://core.ac.uk/download/79616604.pdf
Disclaimer: The views expressed herein are for reference only and don't necessarily represent the official views of Alibaba Cloud.
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