“We invent so you can reinvent”
Viewing the Future of Data and AI from Amazon Web Services re:Invent 2024
The Future of Data and AI: Insights from Amazon Web Services re:Invent 2024
Introduction/Conference Overview
Introduction/Conference Overview
In the era of "All in AI", is it about building a better tool platform or powerful products, empowering oneself or others, and focusing on self-innovation or paving the way for others' innovation? It can be said to be the main long-term strategic logic for the business development of leading tech behemoths.
In the era of "All in AI", technology giants face crucial choices: to build a better tool platform or powerful products, to empower themselves or others, and to focus on self-innovation or pave the way for others' innovation. These are the main long-term strategic logics for the business development of leading tech behemoths.
The emergence of OpenAI and the performance of GPT have undoubtedly had a certain impact on Amazon Web Services (AWS), which has long held a leading position. Many users, attracted by the powerful GPT, have chosen to cooperate with Microsoft to build long-term AI strategies. As the pioneer of the cloud computing industry, AWS has always been a benchmark in product innovation, leadership, and breakthroughs in new technologies. Although it was somewhat late to the game, as we can see, in the truly great development and transformation of technologies and industries, a day or two earlier or later doesn't essentially matter. Accurately finding one's strategic position and bringing value are always the main influencing factors in market competition.
The emergence of OpenAI and the performance of GPT have undoubtedly had a certain impact on Amazon Web Services (AWS), which has long held a leading position. Many users, attracted by the powerful GPT, have chosen to cooperate with Microsoft to build long-term AI strategies. As the pioneer of the cloud computing industry, AWS has always been a benchmark in product innovation, leadership, and breakthroughs in new technologies. Although it was somewhat late to the game, as we can see, in the truly great development and transformation of technologies and industries, a day or two earlier or later doesn't essentially matter. Accurately finding one's strategic position and bringing value are always the main influencing factors in market competition.
Overall, AWS's strategic focus is reflected in the following aspects:
Overall, AWS's strategic focus is reflected in the following aspects:
Helping others innovate, providing users with better toolkits
Empowering Others to Innovate and Offering Better Toolkits
User-Centric: Product optimizations show that several products have been improved to deeply meet user needs. For example, Model Distillation, Bedrock Automated Reasoning check, and multi-agent collaboration. The release of these three functional products demonstrates that since the launch of the Bedrock series last year, a large number of users have been using them. Moreover, AWS quickly responds to user needs during usage and turns common problems into products and solutions.
User-Centric:Product optimizations are deeply tailored to user needs. For example, features like Model Distillation, Bedrock Automated Reasoning check, and multi-agent collaboration have been released. The release of these three functional products shows that since the launch of the Bedrock series last year, a large number of users have been using them. Moreover, AWS quickly responds to user needs during usage and turns common problems into products and solutions.
Choice: Always giving users open and autonomous choices. The released nova series, along with other large models on the platform, provides users with more and better products. Compared to a monopolistic approach to AGI training, how to give users the right to choose, inspire them to develop and apply more diversely, and decentralize the "right to choose"? This core logic behind the products is more adaptable to the flourishing technology landscape.
Choice: Always giving users open and autonomous choices. The released nova series, along with other large models on the platform, provides users with more and better products. Compared to a monopolistic approach to AGI training, AWS focuses on how to give users the right to choose, inspire them to develop and apply more diversely, and decentralize the "right to choose". This core logic behind the products is more adaptable to the flourishing technology landscape.
Long-Term and Forward-Looking Product Strategies: The openness and integrity among products leave room for future technological changes. Sagemaker AI has integrated new generative AI capabilities into the original AI development platform. Sagemaker's past success has made many developers still favor this platform. In the new GenAI era, instead of discarding the original products, quickly integrating new technologies into them is a huge test of technical and product capabilities for both existing product design and new technology development and new product launches. This characteristic can also be seen in the upgrades of many AWS data products.
Long-Term and Forward-Looking Product Strategies: The openness and integrity among products leave room for future technological changes. Sagemaker AI has integrated new generative AI capabilities into the original AI development platform. Given Sagemaker's past success, many developers still favor this platform. In the new GenAI era, instead of discarding the original products, quickly integrating new technologies into them is a huge test of technical and product capabilities for both existing product design and new technology development and new product launches. This characteristic can also be seen in the upgrades of many AWS data products.
Upgrades of Data Products
Upgrades of Data Products
As the key asset for AI and enterprise business optimization, data's importance continues to rise. At the re:Invent 2024 conference, AWS once again emphasized its focus on data infrastructure, providing strong underlying support for enterprises to implement data strategies while continuously upgrading product features to further optimize the user data experience.
Data, as the key asset for AI and enterprise business optimization, is growing in importance. At the re:Invent 2024 conference, AWS once again emphasized its focus on data infrastructure. While providing strong underlying support for enterprises to implement data strategies, it also further optimizes the user data experience through continuous product feature upgrades.
Optimizing Product Linkage to Continuously Improve the Enterprise Data Strategy Implementation Experience：AWS offers comprehensive data processing and analysis tools to support enterprises' business needs at different stages. With the rise of generative AI, the data streams that enterprises face are becoming more complex, requiring multiple analysis tools and machine learning platforms to work together to fully unleash data value. At the same time, the rapid changes in the business environment also demand higher agility in data processing.
Optimizing Product Linkage to Continuously Improve the Enterprise Data Strategy Implementation Experience: AWS offers comprehensive data processing and analysis tools to support enterprises' business needs at different stages. With the rise of generative AI, the data streams that enterprises face are becoming more complex, requiring multiple analysis tools and machine learning platforms to work together to fully unleash data value. At the same time, the rapid changes in the business environment also demand higher agility in data processing.
In the new-generation Amazon SageMaker, SageMaker Unified Studio significantly reduces the cross-platform and cross-product operation burden, enabling smoother team collaboration and more efficient model development and deployment. Therefore, when enterprises face large amounts of data from external sources and AI training, they can process and analyze vast amounts of data more efficiently under the premise of ensuring data security and compliance, achieving a flywheel-like growth from data to business insights and then to business innovation.
In the new-generation Amazon SageMaker, SageMaker Unified Studio significantly reduces the cross-platform and cross-product operation burden through a unified console, enabling smoother team collaboration and more efficient model development and deployment. Thus, when enterprises face large amounts of data from external sources and AI training, they can process and analyze vast amounts of data more efficiently under the premise of ensuring data security and compliance, achieving a flywheel-like growth from data to business insights and then to business innovation.
Moreover, the zero-ETL concept proposed by AWS at re:Invent 2022 is also evolving. Based on the zero-ETL implementation within AWS's internal data products such as Amazon Aurora and Amazon Redshift, it now further extends to support third-party SaaS applications. Based on this, users can directly introduce external application data into data product tools for analysis without building complex data pipelines, providing enterprises with more agile data flow and insight acquisition capabilities.
The zero-ETL concept proposed by AWS at re:Invent 2022 is also evolving. Based on the zero-ETL implementation within AWS's internal data products such as Amazon Aurora and Amazon Redshift, it now further extends to support third-party SaaS applications. As a result, users can directly introduce external application data into data product tools for analysis without building complex data pipelines, providing enterprises with more agile data flow and insight acquisition capabilities.
Enhancing Data Governance Convenience to Support Enterprises in Identifying and Maintaining Data Assets：Amazon SageMaker Lakehouse enables unified management of data lakes, data warehouses, operational databases, and enterprise applications, providing consistent fine-grained access control to ensure data governance. At the same time, through Amazon SageMaker Catalog, users can use metadata created by generative AI for semantic searches, safely discovering, finding, and accessing data and models. Additionally, the newly added Metadata feature in Amazon S3 enables automatic acquisition and real-time update of object metadata, helping enterprises find the data required for business insights, real-time inference applications, etc. more quickly.
Enhancing Data Governance Convenience to Support Enterprises in Identifying and Maintaining Data Assets: AWS launched Amazon SageMaker Lakehouse to achieve unified management of data lakes, data warehouses, operational databases, and enterprise applications, providing consistent fine-grained access control to ensure data governance. Meanwhile, through Amazon SageMaker Catalog, users can use metadata created by generative AI for semantic searches, safely discovering, finding, and accessing data and models. Additionally, the newly added Metadata feature in Amazon S3 enables automatic acquisition and real-time update of object metadata, helping enterprises find the data required for business insights, real-time inference applications, etc. more quickly.
These new features significantly improve the convenience of data governance, enhance the visibility and controllability of data assets, and strongly support users in data governance, business decision-making, and innovation.
These new features significantly improve the convenience of data governance, enhance the visibility and controllability of data assets, and strongly support users in data governance, business decision-making, and innovation.
Data infrastructure continues to innovate to support the sustainable implementation of data strategiesIn the database field, Amazon Cloud Technology has launched re:Invent 2024. Amazon Aurora DSQL, which combines Amazon TimeSync service and Serverless technology capabilities, provides nearly unlimited scalability and 99.999% multi-region availability. At the same time, Amazon DynamoDB global tables have also added multi-region strong consistency support, further strengthening their distributed database service capabilities.
Continuous innovation in data infrastructure to sustain data strategy implementationDatabase Field: Amazon Aurora DSQL, launched at re:Invent 2024, combines the capabilities of Amazon TimeSync service and Serverless technology, providing nearly unlimited scalability and 99.999% multi-region availability. At the same time, Amazon DynamoDB global tables have added multi-region strong consistency support, further strengthening its distributed database service capabilities.
In the field of data storage, Amazon Web Services (AWS) has also made several detailed optimizations, such as adding additional context for HTTP 403 access denied messages to Amazon S3, supporting storage browsers that allow end-users to directly access Amazon S3 data from their applications, and enabling Amazon EBS to create time-based snapshot replicas.
Data Storage Field: AWS has also made multiple detailed optimizations. For example, Amazon S3 has added extra context for HTTP 403 access-denied messages and supports a storage browser, allowing end-users to directly access Amazon S3 data from their applications; Amazon EBS supports creating time-based snapshot copies.
In the face of the deep advancement of enterprise data strategies and the rapid development of the AI era, Amazon Web Services continues to help users efficiently identify, manage, and unlock the value of data through innovation and optimization of data infrastructure technology.
Facing the in-depth advancement of enterprise data strategies and the rapid development of the AI era, AWS is continuously innovating and optimizing data infrastructure technologies to help users efficiently identify, manage, and unleash the value of data.
Upgrading AI products
AI Product Upgrades
Amazon Web Services (AWS) introduced practical AI concepts at the re:invent 2024 conference, aiming to effectively meet users' diverse scenario needs. Based on this concept, AWS has launched full-stack joint innovation and upgrades covering infrastructure, models, and applications.
At the re:Invent 2024 conference, AWS introduced the practical AI concept, aiming to truly meet the diverse scenario needs of users. Based on this concept, AWS has launched full-stack coordinated innovation and upgrades covering infrastructure, models, and applications.
At the infrastructure layer, we continue to solidify the AI computing foundation.To meet users' ever-evolving needs, Amazon Web Services (AWS) has further released P6 compute instances based on NVIDIA Blackwell GPU chips in addition to the 13 compute instances launched in collaboration with NVIDIA. This helps AWS solidify its position as an excellent choice for users who want to use GPU cloud services. Moreover, current compute instances can now fully utilize the self-developed Amazon Trainium2 chip released last year, and four interconnected Trn2 instances, 64 Trainium2 chips, and Trn2 UltraServers with a maximum of 832 million billion floating-point operations per second are now available for preview. Finally, the new Trainium3 chip has been released this time, featuring a 3nm process, a 40% energy efficiency improvement, and a 1x performance boost for the next generation of generative AI workloads.
Infrastructure Layer, Continuously Strengthening the AI Computing FoundationTo meet users' ever-changing needs, AWS has further released the P6 computing instance based on the NVIDIA Blackwell GPU chip, helping AWS solidify its position as an excellent choice for users to use GPU cloud services. In addition, current computing instances can fully adopt the Amazon Trainium2 self-developed chip released last year. The Trn2 UltraServers, with 4 Trn2 instances interconnected via NeuronLink, 64 Trainium2 chips, and a peak performance of83.2 petaflopsare already available for preview. Finally, the newly released Trainium3 chip, with a3nmprocess40%Energy efficiency improvement, and a100%Performance Boost is designed for next-generation generative AI workloads.
The model application and development layer provides users with a high degree of freedom for model selection and use.Based on the concept that 'no single model can be applied to all business scenarios', at this press conference, in addition to further integrating many high-quality large models from the industry, Amazon Bedrock has also made innovative upgrades in functionality. Firstly, the Amazon Bedrock Model Distillation model distillation feature was released. This feature aims to enable small models to be used quickly and effectively in specific scenarios. Compared with original large models, distilled models created for specific use cases not only have a 5-fold speed improvement and a 75% cost reduction but also achieve an accuracy loss of less than 2% in use cases such as RAG. In addition, to prevent factual errors caused by model hallucinations, Amazon Web Services has launched the Amazon Bedrock Automated Reasoning checks feature, which verifies the accuracy of model responses by cross-referencing information provided by customers. Finally, Amazon Web Services has released the Amazon Bedrock multi-agent collaboration feature, enabling customers to build more complex and efficient generative AI applications. This not only improves the system's processing capabilities but also provides customers with more flexible and diverse application options. In addition to the upgrade of Amazon Bedrock, Amazon Web Services has grandly launched the new generation of basic models, the Amazon Nova series. This includes the ultra-fast text generation model Amazon Nova Micro, multimodal models such as Amazon Nova Lite, Amazon Nova Pro, and Amazon Nova Premier that can process text, images, and videos and generate text, as well as Amazon Nova Canvas for generating high-quality images and Amazon Nova Reel for generating high-quality videos. Moreover, Amazon Web Services plans to launch two more Amazon Nova models in 2025 - the 'Speech to Speech' and 'Any to Any' multimodal models.
Model Application and Development Layer, offering high freedom in model selection and useBased on the idea that "no single model fits all business scenarios", at this conference, Amazon Bedrock not only further integrated many high-quality large models in the industry but also achieved innovative upgrades in functionality. First, the Amazon Bedrock Model Distillation function was released. This function aims to enable small models to be used quickly and well in specific scenarios. Compared to the original large models, the distilled models created for specific use cases not only have a5xspeed increase and a75%Cost reduction but also less than2%Accuracy loss is a common issue in use cases like RAG. Secondly, to prevent factual errors caused by model hallucinations, AWS launched the Amazon Bedrock Automated Reasoning checks function, which verifies the accuracy of model responses by cross-referencing information provided by customers. Finally, AWS released the Amazon Bedrock multi-agent collaboration function, enabling customers to build more complex and efficient generative AI applications. This not only improves the system's processing capabilities but also provides customers with more flexible and diverse application options. In addition to the upgrade of Amazon Bedrock, AWS has also launched the new-generation basic model Amazon Nova series, including the ultra-fast text generation model Amazon Nova Micro, the multi-modal models Amazon Nova Lite, Amazon Nova Pro, Amazon Nova Premier that can process text, images, and video and generate text, as well as Amazon Nova Canvas for generating high-quality images and Amazon Nova Reel for generating high-quality video. Moreover, AWS plans to launch two more Amazon Nova models, "Speech to Speech" and "Any to Any" multi-modal models, in 2025.
At the application layer, we help customers improve operational efficiency.Today, enterprise customers generally face the challenge of quickly finding the resources they need amidst massive information. To improve their traditional build experience and simplify complex data tasks, Amazon Web Services has introduced innovative features with the launch of the Amazon Q series products. First, Amazon Q Developer demonstrated a significant performance improvement at this conference, ranking among the top in specialized SWE bench benchmark tests for testing programming capabilities, which can solve54.8%Software development issues. In addition, to meet the different scenario needs of developers, Amazon Q Developer has launched three automated intelligent agents that can be used for automating unit testing, document generation, and code review processes respectively, further improving developer efficiency.
Application Layer: Helping Customers Improve Work EfficiencyCurrently, enterprise customers generally face the challenge of quickly finding the required resources in a vast amount of information. To improve customers' traditional construction experience and simplify complex data tasks, AWS has launched innovative features for the Amazon Q series products. First, Amazon Q Developer has shown significant performance improvement at this conference, ranking high in the SWE bench benchmark test specifically for testing programming capabilities and being able to solve54.8%In addition, to meet the different scenario needs of developers, Amazon Q Developer has launched three automated agents, which can be used to implement automatic unit test execution, document generation, and code review processes respectively, further improving developer efficiency.
The full-stack collaborative innovation and upgrade capabilities of AI products at this re:invent conference demonstrated once again the innovative strength and customer-respecting attitude of Amazon Web Services, confirming the event theme - "We invent so you can reinvent".
The full-stack coordinated innovation and upgrade of AI products at this re:Invent conference, in terms of functions and speed, once again demonstrate AWS's innovation strength and its responsible attitude towards customers, confirming the event theme - "We invent so you can reinvent".
Value to Chinese users
Chinese User Value
Against the backdrop of re:Invent 2024, Amazon Web Services has provided profound value to Chinese users, especially under the dual strategy of global operations and localized support.
In the context of re:Invent 2024, AWS offers profound value to Chinese users, especially under the dual strategies of global operation and local support.
Globalized Operations | Amazon Web Services provides strong support for Chinese enterprises going global and international enterprises localizing their operations.
Global Operation | AWS provides strong support for Chinese enterprises going global and international enterprises localizing in China.
Boosting Chinese Enterprises' Overseas Expansion:
Helping Chinese Enterprises Go Global:
Multi-region compliance support: Amazon Web Services has integrated multiple globally leading large models through services such as Amazon Bedrock to help Chinese enterprises meet data privacy and compliance requirements in different regions. This is particularly crucial for enterprises looking to expand into markets such as Europe and North America.
Multi-Regional Compliance Support: Through services like Amazon Bedrock, AWS integrates multiple world-leading large models to help Chinese enterprises meet data privacy and compliance requirements in different regions. This is particularly crucial for enterprises looking to expand into markets such as Europe and North America.
Generative AI Empowers International Business: Amazon Nova series models, with their high cost-effectiveness and multimodal capabilities, can support various generation tasks such as text, images, and videos, providing strong support for outbound enterprises in content generation and customer interaction.
Generative AI Empowering International Business: Amazon Nova series models, with their high cost-performance ratio and multi-modal capabilities, can support multiple generation tasks such as text, image, and video, providing strong support for outbound enterprises in content generation, customer interaction, etc.
Unified Data Management and Collaboration: The new generation of Amazon SageMaker provides a unified data and AI development environment to help cross-border teams collaborate, enhance data insight capabilities, and adapt to different market demands.
Unified Data Management and Collaboration: The new-generation Amazon SageMaker provides a unified data and AI development environment, facilitating multinational team collaboration, enhancing data insight capabilities, and adapting to different market needs.
Support for localization of foreign-funded enterprises:
Supporting the Localization of Foreign Enterprises:
Amazon Web Services (AWS) provides full-stack technical support (from chips to applications) in China, enabling foreign-funded enterprises to adapt more quickly to the Chinese market demand. For instance, with the new features of Amazon Q Developer, foreign companies can more efficiently complete tasks such as code review and documentation generation, thereby optimizing their local R&D processes.
AWS's full-stack technical support (from chips to applications) in China enables foreign enterprises to adapt to Chinese market needs more quickly. For instance, with the new features of Amazon Q Developer, foreign enterprises can complete tasks such as code review and document generation more efficiently, thereby optimizing their local R & D processes.
Amazon Web Services (AWS) deeply invests in the Chinese market, collaborating with domestic developers and enterprises to promote technological innovation through nationwide exhibition activities and strengthening interaction with the local ecosystem.
AWS has been deeply cultivating the Chinese market, cooperating with domestic developers and enterprises, promoting technological innovation through national tour activities, and strengthening interaction with the local ecosystem.
Localization Support | Amazon Web Services creates localized solutions based on products and services to help drive industry innovation.
Local Support | AWS builds local solutions based on its products and services to drive industry innovation.
Optimize costs and performance:
Optimizing Cost and Performance:
Amazon Nova models significantly reduce training and inference costs (up to a 75% saving), and improve inference speed. This cost-effective solution is ideal for budget-sensitive small and medium-sized enterprises as well as startups.
Amazon Nova series models significantly reduce training and inference costs (up to 75% savings), and increase inference speed. This cost-effective solution is very suitable for small and medium-sized enterprises and startups with tight budgets.
The new computing instance based on the self-developed chip Trainium3 further reduces the total cost of ownership for AI workloads, providing local enterprises with a more competitive computing power option.
The new computing instances based on the self-developed Trainium3 chip further reduce the total cost of ownership for AI workloads, providing local enterprises with more competitive computing power options.
Tailored to local scenario needs:
Meeting Local Scene Requirements:
Amazon Bedrock's multi-agent collaboration feature and automatic reasoning check feature help Chinese enterprises build more reliable generative AI applications in complex scenarios. These features are particularly suitable for business scenarios that require high personalization and accuracy, such as retail, e-commerce, finance, and other industries.
Amazon Bedrock's multi-agent collaboration and automatic reasoning check functions help Chinese enterprises build more reliable generative AI applications in complex scenarios. These functions are especially applicable to business scenarios in industries such as retail, e-commerce, and finance that require high personalization and accuracy.
To meet the modernization transformation needs of traditional industries, Amazon Q Developer has introduced tools specifically designed for migrating large-scale machines and VMware workloads, accelerating the digital transformation process of local enterprises.
To meet the modernization transformation needs of traditional industries, Amazon Q Developer has introduced tools specifically for migrating mainframe and VMware workloads, accelerating the digital transformation process of local enterprises.
It not only meets the strict requirements of the Chinese market for data privacy, security, and compliance but also provides strong technical support for enterprises during their digital transformation by optimizing performance, reducing costs, and enhancing efficiency. The deeply customized local service model is driving innovation and development in multiple industries including life sciences, automotive, retail, and more.
AWS not only meets the strict requirements of data privacy, security, and compliance in the Chinese market but also provides strong technical support for enterprises in the digital transformation process by optimizing performance, reducing costs, and improving efficiency. The deeply customized local service model is driving innovation and development in multiple industries, including life sciences, automobiles, and retail.
Amazon Web Services (AWS) has provided significant value to Chinese users through its global resource integration capabilities and customized optimization for the Chinese market. From helping enterprises go global to empowering local innovation, AWS not only provides leading technologies but also creates practical business value for users by reducing costs, improving efficiency, and strengthening ecological cooperation. In the future, as generative AI technology matures further, these innovations will continue to drive the competitiveness of Chinese enterprises on the global stage and accelerate the digital transformation process of various industries.
Through its global resource integration capabilities and customized optimizations for the Chinese market, AWS provides significant value to Chinese users. From facilitating going global to empowering local innovation, AWS not only offers leading technologies to enterprises but also creates practical business value for users by reducing costs, improving efficiency, and strengthening ecological cooperation. In the future, as generative AI technology further matures, these innovations will continue to boost the competitiveness of Chinese enterprises on the global stage and accelerate the digital transformation process of various industries.
Frost & Sullivan's insights into future trends in data and AI development
Frost & Sullivan's Insights into the Future Trends of Data and AI Development
The future development trend of the integration of data and AI is reflected in the following three aspects:
The future development trends of the combination of data and AI are reflected in the following three aspects:
Data governance is moving towards integration and intelligence.Under the general trend of integrating data with AI, the emergence of cloud-native data governance platforms has met the demand for AI-driven data governance. In terms of data management, AI tends to be an efficient add-on tool. With the support of AI, unified data management across different data systems can be achieved, thereby further breaking down the barriers caused by different data systems. For enterprises that use different data systems for storage, this not only further reduces management costs in this area but also facilitates data operations for managers. This further achieves the goal of making data management simple and efficient through AI.
Data Governance Is Moving Towards Integration and IntelligenceUnder the big trend of combining data and AI, the emergence of cloud-native data governance platforms meets the need to govern data with AI. In data management, AI is more like an efficient additional tool. With the help of AI, unified data management of different data systems can be achieved, further breaking down the data silos caused by different data systems. For enterprises that use different data systems for storage, this can not only further reduce management costs in this regard but also make it more convenient for managers to operate data. This also further achieves the goal of making data management simple and efficient with AI.
AI is lowering the threshold for data analysisAnother direction combining data and AI is the integration of AI with data development tools, where AI generates the data analysis results required by users through natural language input. Although the technology is not yet fully mature, many manufacturers have begun experimenting with this type of product. The core goal is to simplify data processing and the AI technology stack through AI, thereby reducing the learning curve for data analysis for beginners or those with no prior knowledge. This trend reflects AI's attempt to make data analysis easier to grasp and operate.
AI is Lowering the Threshold for Data AnalysisAnother direction of combining data and AI is the integration of AI and data development tools. With natural language input, AI can generate data analysis results that users need. Although the technology is not yet fully mature, many manufacturers have already started experimenting with this type of product. Its core purpose is to simplify data processing and the AI technology stack with AI, thereby reducing the usage burden on beginners or those without a technical background. This trend indicates that AI is striving to make data analysis more accessible and user-friendly.
The importance of data centers has become significant due to the integration of data and AIIn terms of hardware, due to the integration of data and AI, the requirements for data centers have become more stringent. Data centers themselves need to meet conditions such as computing power scale and architecture suitable for AI integration, data management and flow, and resource pool scheduling. As a result, data centers that are structurally outdated and do not meet these conditions have to undergo significant structural changes. The importance of data centers is highlighted in this development trend. Under the development trend of data and AI, how to design data centers reasonably and efficiently while storing high-growth data demands will become an issue that manufacturers need to consider in the future.
The Importance of Data Centers Is Growing Due to the Combination of Data and AIIn terms of hardware, due to the combination of data and AI, the requirements for data centers have become more stringent. Data centers themselves need to meet conditions such as computing power scale and architecture suitable for AI integration, data management and dredging, and resource pool scheduling. Data centers with backward structures that do not meet these conditions have to undergo significant structural changes. The importance of data centers has emerged under this development trend. In the development trend of data and AI, how to design data centers to be reasonable, efficient, and capable of storing data with high growth requirements will be a problem that manufacturers need to consider in the future.
Frost & Sullivan Independent Research Group
