祝融说。

Background: The Leap from Informatization to Intelligence

The history of human civilization is, to a large extent, a history of the evolution of tools, and the core driving force behind this evolution is the continuous leap in information processing capabilities. Looking back over the past half century, the digital transformation of enterprises and even society as a whole can be roughly divided into three closely connected, progressively deepening stages: digitization, informatization, and intelligence. Understanding the essential differences and evolutionary logic of these three stages is the logical starting point for our discussion of today's computing revolution.

Phase 1: Digitization — The Bit Mapping of the Physical World

Digitization is the origin of transformation. Its core task is to convert various types of information from the physical world — text, images, sound, processes — into the "0"s and "1"s that computers can recognize and process, i.e., bit streams. This is a process of mapping from atoms to bits. Within an enterprise, this manifests as the electronification of documents, the CADization of drawings, and the online migration of business forms. The main goal of this stage is "recording" and "storage," replacing traditional manual transcription and paper archiving through technical means, and solving the basic problem of information preservation and retrieval. For the power grid, early digitization is reflected in moving the geographic topology maps, equipment ledgers, and operation procedures of the grid from paper to computers. This is a foundational but crucial stage, accumulating the most primitive "digital mineral deposits" for subsequent informatization and intelligence. However, the information in this stage is static and isolated — mere mirrors of the physical world, not yet endowed with flowing life and connected wisdom.

Phase 2: Informatization — Process-Driven Business Online Operation

Informatization builds upon digitization, using network technologies and software systems to connect isolated data and deeply bind it with the enterprise's core business processes, achieving online, process-oriented, and automated operations. The core of this stage is "connection" and "process." Management information systems represented by ERP (Enterprise Resource Planning), OA (Office Automation), CRM (Customer Relationship Management), and SCADA (Supervisory Control and Data Acquisition) are the hallmark products of the informatization era.

In the informatization stage, the enterprise's goal is to improve efficiency and standardize management. Through information systems, data flows in an orderly manner between different departments and links, breaking down some information barriers, solidifying standard operating procedures, and greatly improving operational efficiency and management levels. At State Grid, informatization construction has achieved brilliant results: the dispatch automation system realized centralized monitoring and remote operation of the power grid; the marketing management system supported electricity bill calculation and collection for hundreds of millions of users; the Production Management System (PMS) managed the full life cycle of massive equipment assets.

However, the essence of the informatization era remains "process-driven" and "human-computer interaction." Systems execute tasks according to preset rules and processes. The value of data is mainly reflected in supporting process operation and providing reports and data for human decision-making. The system itself lacks the ability to autonomously "think" and "make decisions." Facing increasingly complex external environments and massive data growth, the limitations of informatization systems are also becoming apparent: rigid rules struggle to handle sudden and unknown situations; limited data analysis capabilities, mostly remaining at shallow statistics and display, unable to uncover deep correlations and patterns; although systems are interconnected, deep data fusion and value creation remain insufficient. The endpoint of informatization is the construction of an efficient "digital nervous system," but this system still needs a "brain" to command it.

Phase 3: Intelligence — Data-Driven Emergence of Wisdom

Intelligence is precisely the revolutionary leap that installs a "brain" into this vast digital nervous system. If informatization is "process-driven," then the core of intelligence is "data-driven." It is no longer satisfied with merely executing preset rules; it strives to autonomously learn patterns and discover insights from massive, multidimensional, real-time data, and based on these insights, making predictions, judgments, and even decisions, thereby achieving self-adaptation, self-optimization, and even self-reconfiguration of business processes.

The core engine of this leap is artificial intelligence (AI), especially the new generation of AI technology represented by deep learning. The fuel driving the continuous operation of the AI engine is big data; the platform carrying all this is cloud computing; the tentacles connecting all things are the Internet of Things. The convergence of these technologies has jointly given rise to the wave of intelligence.

The essence of intelligence is to enable machines to imitate or even surpass human intelligence in certain cognitive domains, achieving a leap from "perceptual intelligence" (such as image recognition, speech recognition) to "cognitive intelligence" (such as natural language understanding, knowledge reasoning, decision analysis). For enterprises, this means:

1. From Assisting Decision-Making to Assisting and Partially Replacing Decision-Making: In some complex scenarios, AI models can generate candidate dispatch plans, maintenance plans, and marketing strategies whose precision and efficiency approach or exceed human experts on certain tasks, achieving a transformation from "human brain + computer" to "AI-brain-assisted decision-making."
2. From Passive Response to Active Prediction: Through deep learning of historical and real-time data, the system can predict when equipment may fail, when customers may churn, and what the next day's new energy output curve will be, achieving a transformation from "post-event remediation" to "pre-event warning" and "active intervention."
3. From Insight into the Past to Creation of the Future: Technologies represented by generative AI can not only analyze data but also create new content. For example, in grid planning and design, AI can automatically generate multiple feasible design schemes based on constraint conditions; in emergency plan development, AI can simulate unknown disasters and generate targeted response strategies.

National Strategic Support in the Wave of New Infrastructure

This leap from informatization to intelligence is not merely a spontaneous corporate behavior; it resonates with the national top-level strategy. In recent years, China has clearly proposed accelerating "New Infrastructure Construction." The essence of New Infrastructure is the core foundation of the intelligent era, mainly including "connection" infrastructure represented by 5G, the Internet of Things, and the Industrial Internet, and "computing power" and "intelligence" infrastructure represented by AI, cloud computing, and big data centers.

The essence of "New Infrastructure" is to provide basic power for the entire digital economy. If traditional infrastructure (railways, highways, airports) was the artery of the industrial economy, then New Infrastructure is the "new artery" of the digital economy. In this grand blueprint, computing infrastructure has been placed in an unprecedented core position. It is no longer an accessory of the IT industry but, like water, electricity, roads, and networks, a basic resource supporting the operation and innovation of the entire society. The comprehensive launch of the national "East Data West Computing" project has elevated the construction of computing infrastructure to a strategic height of national resource optimization allocation.

For State Grid, this leap from informatization to intelligence is both a mission to respond to the national New Infrastructure strategy and promote the development of the digital economy, and an internal need to respond to energy transformation, ensure energy security, and achieve high-quality development. As a key platform carrying energy flow and information flow, the power grid is itself an important component and application scenario of New Infrastructure. Therefore, actively embracing the computing revolution and building a powerful AI computing foundation is not only going with the flow but also a necessary and must-seize strategic high ground.

In summary, we are at a great turning point. The massive "mineral deposits" of data accumulated during the informatization era are ready; the powerful "engine" of AI algorithms for the intelligent era has been invented; and the starting pistol of the national New Infrastructure strategy has been fired. Everything is ready, and only the strong east wind of "computing power" is needed. Understanding this historic leap is the fundamental premise for understanding why we invest so much effort in building, operating, and maintaining an enterprise-level AI computing platform.

Challenges: Three Core Problems Enterprises Face in AI Computing Construction

The grand strategic blueprint must be rooted in the soil of reality. On the journey from informatization to intelligence, especially when building the AI computing platform that serves as core support, enterprises generally encounter a huge gap between theory and practice. As a key infrastructure enterprise with a huge scale, complex structure, and extremely high safety requirements, State Grid deeply experienced the "three mountains" standing between ideal and reality in its early explorations. These three core challenges are the difficult problems that all enterprises committed to AI transformation must face and solve, and they are also the core targets that the subsequent chapters of this book address.

The First Mountain: The Management "Black Box" — Disorder and Inefficiency in Resource Operations

This is the most common and most easily overlooked challenge. In the early stages of a project, decision-makers and technical teams often focus on hardware selection and procurement — how many and what type of GPU cards to purchase, the configuration of servers, the network bandwidth. This mindset of "emphasizing construction over operations" leads computing resources, once deployed, to quickly fall into a "black box" state of management and operations, where internal resource flows, usage efficiency, and cost-effectiveness become vague and unclear, ultimately resulting in huge "invisible waste."

"Chaos and Disorder" on the Demand Side

The source of computing demand is business. However, business experts understand scenarios, and algorithm engineers understand models, but they often find it difficult to accurately answer the question: "How much computing power does my task actually need?" This leads to huge uncertainty in the demand submission stage. Some teams estimate based on experience, applying for 100 card-hours of resources but actually using only 10 card-hours; others, to be safe, apply for exclusive resource pools but utilize less than 10% of the time. Facing these vague demands, management departments lack scientific metrics and evaluation tools, and approval decisions often depend on the "voice" of the applicant rather than the actual priority of the business and the precision of resource needs, causing resource allocation to lose its foundation of fairness and efficiency from the very source.

"Process Obstruction" on the Supply Side

In the absence of a unified operations platform, the allocation, modification, and recovery of computing resources often rely on fragmented, manual processes. Users need to fill out paper or electronic approval forms, go through multiple layers of approval, and then the system administrator manually logs into the backend to configure environments and grant permissions. The entire process is time-consuming and labor-intensive, taking anywhere from days to weeks, completely unable to match the agile iterative rhythm of AI application development. Even more critical is the lack of recovery mechanisms. When a project ends or a model iteration is completed, the computing resources it occupies, lacking proactive recovery processes and mechanisms, become "zombie resources," idling indefinitely. These "sleeping" computing resources, on one hand, occupy valuable rack space, power, and O&M resources; on the other hand, new, more urgent demands have to enter long waiting queues, forming a vicious cycle of "some die of drought while others die of flood."

"Blind Men and the Elephant" on the Monitoring Side

Both managers and users urgently want to understand the usage of computing power, but often "want to see but cannot." Managers cannot obtain a global, real-time resource view, cannot know the resource level, average utilization, peak and valley values of the entire platform, and cannot provide data support for capacity planning and investment decisions. Users do not know why their submitted tasks are running slowly — is the bottleneck in computing, storage, or networking? Is it a code efficiency issue or insufficient resource allocation? Without effective monitoring and diagnostic tools, performance optimization is impossible, and user experience is greatly compromised. The entire computing platform is like a complex machine roaring but with a broken dashboard — although running, no one knows its health status or true efficiency. Several industry sampling estimates of enterprise GPU clusters (with varying scopes and time points) indicate that without refined operations, average resource utilization on enterprise AI computing platforms tends to sit at a low level of around twenty percent, meaning a substantial share of computing investment sits idle or underused (this is a range-level, synthetic description rather than precise statistics) — a sunk cost no enterprise can bear.

The Second Mountain: The "High Wall" of Usage — The Steep Threshold of Technology Application

If the management black box is a resource efficiency problem, then the technology high wall is a capability popularization problem. AI computing, especially GPU-based heterogeneous computing, is far more complex to use and master than traditional CPU computing, erecting a formidable "high wall" for the vast majority of non-professional business technical personnel. This wall greatly hinders the large-scale implementation and universal development of AI technology within enterprises.

The "Layer Cake" of Environment Configuration

The runtime environment for a typical AI training task is a complex, multi-layered "layer cake." The bottom layer is the physical server and GPU hardware, requiring specific versions of firmware and drivers to be installed; above that is the operating system and CUDA (Compute Unified Device Architecture) toolkit, the bridge connecting hardware and upper-layer software; then comes the containerized environment like Docker for isolating and packaging applications; and the top layer consists of deep learning frameworks like PyTorch and TensorFlow, along with hundreds of Python dependency libraries. These components have strict version dependencies. A mismatch at any level can prevent the environment from starting or cause runtime errors. For a user who only wants to focus on business logic and model algorithms, spending a great deal of time on environment configuration and dependency conflict resolution is a huge drain of energy and a barrier to innovation.

The "Alchemy" of Performance Optimization

Simply making the model "run" is far from enough. How to make it "run fast and run economically" is key to determining whether an AI application can be economically viable. This involves a series of highly specialized performance optimization techniques akin to "alchemy." For example, how to choose the optimal distributed training strategy (data parallelism, model parallelism, pipeline parallelism, tensor parallelism) based on the model characteristics and cluster scale? How to enable mixed-precision training to greatly increase speed and save memory without significant accuracy loss? How to optimize the I/O pipeline for data preprocessing and loading to avoid the CPU becoming a bottleneck and ensure the GPU is "fed"? How to perform operator fusion or compilation optimization through computation graph analysis? For the vast majority of developers, this knowledge belongs to the category of "dragon-slaying skills," with an extremely high threshold for mastery.

The "Last Mile" of Engineering Implementation

Successful model training is only the beginning of the AI application lifecycle. How to deploy it efficiently, stably, and at scale as an online inference service is the more challenging "last mile." This includes model lightweighting (quantization, pruning, distillation) to adapt to the resource constraints of the inference environment; choosing the appropriate inference engine and performing deep optimization; service packaging, deployment, elastic scaling, canary release, A/B testing; and building a comprehensive monitoring and alerting system to ensure the service SLA (Service Level Agreement). This series of complex MLOps (Machine Learning Operations) tasks requires professional software engineering and operations capabilities, which are often the knowledge gap of algorithm teams.

This invisible "high wall" can ultimately reduce the computing platform to an exclusive tool for a few "AI experts," while the vast majority of "civilian developers" with rich business knowledge and innovative potential are kept outside. The value of AI thus cannot be fully released in the "capillaries" of the enterprise, and its empowering effect is greatly diminished.

The Third Mountain: The "Silos" of Collaboration — Barriers and Divisions in System Linkage

For a large enterprise with a typical "group-province-city" hierarchical structure like State Grid, the collaboration problem is particularly prominent. In the early stages of intelligent transformation, due to the lack of unified planning, units and departments at all levels often "fight their own battles" according to their own needs, independently carrying out AI computing construction and application exploration. This model has some flexibility in the early stage, but as applications deepen, its drawbacks become increasingly apparent, eventually forming isolated "computing silos" and "application chimneys."

"Resource Inefficiency" from Duplicate Construction

Headquarters builds, provincial companies build, and even some business departments build on their own. Different units may purchase hardware from different manufacturers, build heterogeneous technology platforms, and follow varying standards and specifications. This not only causes huge duplicate investments but also, due to the inconsistency of the tech stack, makes subsequent interconnection and unified management extremely difficult. At the same time, the "tidal effect" of business loads is amplified under the silo model: the computing resources of Province A may be at full capacity due to a large training task, while the computing cluster of Province B may be idle, but resources cannot be flexibly transferred between them, resulting in systemic resource mismatch and waste.

"Intangible Loss" of Experience and Wisdom

In silos, the accumulation and sharing of knowledge and experience are severely hindered. Unit A may have spent weeks solving a driver compatibility problem for a specific hardware, accumulating valuable expertise, but this experience cannot be conveniently passed to Unit B facing the same problem. Unit C developed an excellent transmission line defect identification model, but due to differences in deployment environments and data interfaces, Unit D cannot directly reuse it and has to "reinvent the wheel." This kind of low-level repetitive work greatly slows down the technological progress and innovation efficiency of the entire company, preventing the formation of a knowledge compound effect where "one person plants a tree, everyone enjoys the shade."

"The Fence Trap" of Strategic Cohesion

Most critically, the siloed computing system cannot support major company-level strategic tasks. For example, training a "Guangming Electric Power" foundational large model covering the entire network's business and integrating massive multimodal data may require mobilizing tens of thousands of GPU cards for months of collaborative training. Such a "national instrument" level task cannot be independently undertaken by any single silo. Only by logically unifying and scheduling the computing resources of the entire company to form a powerful "computing power grid" can we concentrate forces to accomplish major undertakings, overcome those strategic challenges that determine the future core competitiveness of the enterprise.

These three mountains — the management "black box," the usage "high wall," and the collaboration "silos" — are the fundamental obstacles restricting enterprise AI computing construction from moving from "usable" to "easy to use," from "bonsai" to "landscape." Recognizing the depth and systemic nature of these challenges is the practical basis for formulating our subsequent vision and overall construction approach.

Vision: Building an Integrated, Highly Efficient, Easy-to-Use AI Computing Foundation

The purpose of facing challenges is to transcend them. After deeply analyzing the three core problems facing AI computing construction, we must draw a clear, grand, and achievable blueprint for the future computing platform. This blueprint is our vision. It is not only a direct response to the problems but also a forward-looking definition of the future core infrastructure form of the enterprise in the intelligent era. Our vision can be condensed into three keywords: integration, high efficiency, and ease of use.

Vision 1: Integration — Breaking Barriers, Connecting the Whole with a "Computing Power Grid"

"Integration" is a direct response to the "collaboration silo" problem. Its goal is to build a logically unified, physically distributed "computing power grid" where resources can be globally shared and scheduled, achieving "one network" management of the company's computing resources.

Resource Integration

This is the physical foundation of "integration." We will use advanced cloud-native technologies and a unified resource management platform (such as Kubernetes + GPU Operator) to centrally manage the heterogeneous computing resources (different manufacturers, different models of GPUs) distributed across headquarters and provincial companies, shielding underlying hardware differences, and forming a logically unified, huge, elastic "computing resource pool." When users use it, they don't need to care about which physical server their task runs on; the platform will intelligently schedule based on task requirements and resource status. This will completely break down physical boundaries, enabling on-demand flow and peak-shaving across the entire company, maximizing resource utilization.

Platform Integration

This is the application foundation of "integration." We will build a unified PaaS platform covering the full lifecycle of AI applications (data preparation, model development, model training, model management, inference deployment, application monitoring). No matter where users are located, they can obtain full-stack services from the development environment and training framework to deployment tools through a unified portal and consistent interface. The platform will provide standardized data interfaces, model interfaces, and service interfaces, ensuring that applications and models developed in one place can be seamlessly migrated and deployed to another, enabling the free flow and efficient reuse of technology assets.

Management and Service Integration

This is the organizational guarantee of "integration." We will establish a "headquarters-province" two-level collaborative operations service system. The headquarters is responsible for formulating unified standards and specifications, operational processes, service catalogs, and technology roadmaps, and providing high-level technical support and capability output. Provincial operations teams act as the "front desk," close to the business frontline, responsible for accepting local demands, conducting preliminary review, and providing basic support, while feeding back common issues and high-level demands to the headquarters. Through a unified ticketing system, knowledge base, and communication mechanisms, we form a responsive, clearly divided, and seamlessly coordinated "integrated" service network, providing all users with standardized, reliable service quality.

Vision 2: High Efficiency — Lean Operations, Value-Driven "Computing Engine"

"High efficiency" is the core solution to the "management black box" problem. Its goal is to introduce lean production and data-driven concepts into computing operations, transforming the computing platform from a cost center into a "computing engine" that can continuously create value, with measurable and optimizable efficiency.

Resource Turnover Efficiency

We will greatly shorten the turnover cycle of computing resources through automated allocation and recovery mechanisms. When a training task ends, resources are immediately released; when an inference service goes offline, resources are automatically recovered. For resources with prolonged low load, the system will automatically warn and prompt recovery. Our goal is to minimize the average idle time of computing resources, keeping them in efficient turnover and use like shared bicycles, thereby supporting more business innovation under limited investment.

Development and Operations Efficiency

We will empower the development and operations of AI applications through platform-based and tool-based approaches. Provide pre-optimized development environments for "out-of-the-box" use; provide automated distributed training and hyperparameter search tools to free algorithm engineers from tedious engineering details; provide one-click model deployment and automated elastic scaling capabilities to make application launch and operations simple and reliable. Our goal is to shorten the cycle from idea to launch from "months" to "weeks" or even "days," achieving true agile development and rapid iteration.

Investment Return Efficiency

We will establish a comprehensive, data-driven computing operations evaluation system. Through the "computing display" mechanism, we will monitor in real time and regularly publicize core efficiency indicators including resource utilization, task queuing time, training cost (unit FLOPS cost), and inference latency. These data not only provide the basis for managers' optimization decisions but also provide a yardstick for users to measure the efficiency of their own applications, driving the entire workforce to develop a cost-consciousness of "using computing power carefully and economically," ensuring that every cent of investment generates maximum business return.

Vision 3: Ease of Use — Lowering the Threshold, Universal "Computing Tap Water"

"Ease of use" is the fundamental solution to the "technical high wall" problem. Its goal is to provide computing services like "tap water," so that any employee with innovative ideas within the enterprise, regardless of their AI technical background, can conveniently and quickly access and use AI capabilities, achieving the "universalization" of AI technology.

Convenience of Access

We will provide diverse access methods to meet the needs of different levels of users. For beginners and business personnel, we will provide a "zero-code/low-code" graphical modeling platform for completing data analysis and model training through drag-and-drop operations. For professional algorithm engineers, we will provide powerful JupyterLab development environments and command-line tools, giving them maximum flexibility.

Unconsciousness of Process

The platform will maximally shield the underlying technical complexity. When a user submits a training task, they don't need to worry about the implementation details of distributed training or the handling mechanism of fault recovery — the platform will automatically handle all this. When a user deploys an inference service, they don't need to worry about container packaging, service registration and discovery, or traffic load balancing — the platform will do it with one click. Our goal is to allow users to focus 100% of their energy on business logic and model innovation, rather than dissipating it on underlying technical details.

Richness of Services

In addition to basic computing resources and development tools, the platform will also build a rich "Model and Application Market." We will provide pre-trained models developed internally and verified (such as visual large models for the transmission field, NLP large models for the marketing field) as basic services to all users, supporting them to fine-tune and quickly build their own applications. We will also introduce high-quality models and algorithms from external partners, forming an open, diverse service ecosystem.

In summary, the three visions of "integration," "high efficiency," and "ease of use" together depict the portrait of our ideal AI computing foundation. It is both a powerful, unified, and efficiently operating "computing engine" and an open, convenient, and accessible "empowerment platform." The realization of this vision will fundamentally solve the three major challenges enterprises face in AI computing construction, providing a solid, reliable, and future-oriented digital foundation for the intelligent transformation of State Grid and more large enterprises.

Overall Construction Approach: Five in One — Management, Planning, Technology, Promotion, and Ecosystem

A grand vision requires a clear path to realization. In order to turn the blueprint of "integration, high efficiency, and ease of use" into reality, we have proposed a systematic, multi-dimensional co-promotion overall construction approach, i.e., the "five in one" of management, planning, technology, promotion, and ecosystem. These five dimensions, like the five structural pillars supporting a grand edifice, are indispensable and complementary, together forming the action program for building and operating our AI computing platform. They also provide an overall logical framework for the content of the subsequent chapters of this book.

The First Pillar: Management — Building "Rules and Order" for Operations

Management is the cornerstone of the healthy and sustainable operation of the computing platform and the institutional guarantee for realizing the vision of "high efficiency." Its core goal is to establish a set of scientific, standardized, and transparent "rules of the game" to ensure the fairness, orderliness, and efficiency of computing resource allocation and usage. If technology is the "muscle" of the platform, then management is its "skeleton" and "nervous system."

Core Tasks:

1. Establish an organizational system: Build a "headquarters-province" two-level collaborative operations team, clarifying the responsibility boundaries, collaboration processes, and assessment mechanisms of the teams at each level.
2. Formulate standards and specifications: Develop standards and specifications covering the full life cycle of computing power, including demand estimation standards, resource allocation and recovery specifications, usage evaluation standards (differentiating between training and inference), Service Level Agreements (SLAs), etc.
3. Improve processes and mechanisms: Design and solidify standardized online processes for computing demand acceptance, evaluation, approval, allocation, adjustment, and recovery, achieving automated and process-oriented operations.
4. Strengthen assessment and incentives: Establish assessment and incentive mechanisms for operations personnel, users, and external service vendors, using effective reward and punishment measures to guide all parties in jointly maintaining the healthy operation of the platform.

The construction of the management system aims to fundamentally solve the "management black box" problem, transforming computing operations from an extensive, passive resource supply into a refined, proactive, data-driven service governance.

The Second Pillar: Planning — Ensuring "Troops and Provisions" for Development

Planning is the prerequisite for the computing platform to adapt to future business development and maintain sustained competitiveness. Its core goal is to establish a forward-looking, systematic resource planning mechanism to ensure that the supply of computing resources can match the growth of business demand, achieving a balance between "moderate advancement" and "precise investment."

Core Tasks:

1. Demand consolidation and forecasting: Establish normalized demand collection and forecasting mechanisms, regularly (such as quarterly, annually) consolidating the medium-to-long-term computing demands of headquarters and various units, forming a unified demand view.
2. Capacity planning and design: Based on demand forecasting, combined with technological development trends and cost-benefit analysis, scientifically plan the computing scale, technical architecture, and construction rhythm of the two-level intelligent computing centers.
3. Expansion tracking and guidance: Formulate standardized computing expansion processes and typical plans, track and urge units to complete resource expansion according to plan, and coordinate to solve problems encountered during the expansion process.
4. Existing resource management and optimization: Conduct comprehensive inventory and assessment of existing computing resources within the company, formulate management standards and work plans, and gradually integrate them into the management system, achieving reuse and intensive use.

The construction of the planning system aims to solve the blindness and lag in resource supply, ensuring that our "troops and provisions" are always sufficient to support the growing "battles" of intelligent business.

The Third Pillar: Technology — Creating "Tools and Platforms" for Empowerment

Technology is the carrier for realizing the core functions of the computing platform and the vision of "ease of use." Its core goal is to build a technically powerful, high-performance, user-friendly technology platform and service system, encapsulating underlying complexity and opening up convenient capabilities, providing users with comprehensive technical support.

Core Tasks:

1. Build a PaaS platform: Create a unified AI platform providing toolchains and services covering the full lifecycle of model development, training, and deployment, including development environments, distributed training frameworks, model management, and inference service engines.
2. Strengthen technical support: Assemble a professional technical support team to provide end-to-end expert services from resource configuration consulting and cluster deployment optimization to model adaptation tuning and application performance assurance.
3. Ensure platform stability: Establish a comprehensive monitoring, alerting, and operations system, conducting comprehensive health assessment and high-reliability assurance for computing, storage, and network resources of the platform, ensuring stable operation.
4. Lead technological innovation: Continuously track cutting-edge technologies (such as large models, heterogeneous computing, computing networks), conducting pre-research and pilot testing to ensure the advanced nature of the platform's technical architecture.

The construction of the technology system aims to completely knock down the "high wall of usage," allowing AI capabilities to flow like "tap water" to every corner of the enterprise.

The Fourth Pillar: Promotion — "Publicity and Empowerment" for Value Realization

Promotion is the "last mile" connecting platform capabilities with business value. Its core goal is to establish an effective promotion and empowerment mechanism, allowing more users to "know the platform, be willing to use it, and achieve results with it," ultimately transforming the platform's potential into tangible business outcomes.

Core Tasks:

1. Service catalog and publicity: Compile and continuously update the "Intelligent Computing Operations Service Catalog," promoting it through various online (such as portal websites, public accounts) and offline (such as presentations, roadshows) channels to the entire company.
2. User training and empowerment: Regularly organize training activities at different levels, from basic platform use and general AI education to advanced performance optimization, to improve users' technical level and application capabilities.
3. Results compilation and incentives: Establish a mechanism for discovering, cultivating, and publicizing excellent application cases, regularly selecting and commending "computing application benchmarks," compile "Results Case Collections," form demonstration effects, and incentivize more innovation.
4. Knowledge precipitation and sharing: Build and maintain a computing operations knowledge base, systematically accumulate and share knowledge assets such as best practices, technical documentation, and FAQs, reducing the learning cost for new users.

The construction of the promotion system aims to solve the problem of "good wine but deep alley," accelerating the platform's value realization and innovation cycle through active user operations.

The Fifth Pillar: Ecosystem — "Friends and Partners" for Gathering External Forces

The ecosystem is the source ensuring the long-term vitality and advancement of the computing platform. Its core goal is to step outside the enterprise's internal perspective, connect and gather external wisdom, technology, and resources through openness and cooperation, and build a symbiotic, co-prosperous computing innovation ecosystem.

Core Tasks:

1. Build an expert think tank: Hire top internal and external experts to form an expert committee, providing consultation and guidance for the platform's technical path and strategic development.
2. Develop partners: Establish strategic cooperation with leading domestic and international hardware manufacturers, software companies, algorithm enterprises, and research institutions, engaging in deep collaboration in technology R&D, product integration, and market promotion.
3. Build a developer community: Establish an active internal developer community, encouraging communication and mutual assistance among users; externally, at the right time open some platform capabilities in the form of APIs, attracting external developers to jointly innovate.
4. Establish an evaluation system: Design a scientific evaluation indicator system for ecosystem construction effectiveness, regularly assess cooperation results, and continuously optimize ecosystem operations strategies.

The construction of the ecosystem system aims to solve the limitations of "working behind closed doors," ensuring that our computing platform always stands at the forefront of technology and innovation through "bringing in" and "going out."

Summary

"Management," "Planning," "Technology," "Promotion," and "Ecosystem" — these five dimensions do not exist in isolation but constitute an organic, interconnected whole. Planning is the leader, guiding the direction and rhythm of development; technology is the core, providing the ability to realize value; management is the guarantee, ensuring the health and efficiency of the system; promotion is the goal, driving the implementation of capabilities and the closure of the value loop; and the ecosystem is the future, expanding the boundaries of continuous innovation and development. Together, they form a dynamically evolving, continuously optimizing "flywheel." Once started, through mutual reinforcement, they will drive the enterprise's intelligent transformation forward, steadily and far. This "Five-in-One" construction approach will serve as a main thread throughout this book.