result.yml

# yaml-language-server: $schema=https://github.com/ghraw/cncf/landscape2/refs/heads/main/docs/config/schema/data.schema.json

landscape:
  - category:
    name: Provisioning
    subcategories:
      - name: Automation & Configuration
        items:
          - item:
            name: 12-迈向具身智能规模化：智元机器人基于 OpenYurt 的云边协同实践-蒋晨曦（智元机器人）、唐炳昌（阿里云）.md -
              OpenYurt
            description: 
              面向运维工程师、架构师和技术管理者。该演讲主要介绍了智元机器人基于OpenYurt的云边协同实践，属于Provisioning分类中的基础设施自动化配置和管理。演讲中，OpenYurt作为云边协同基础平台，与Dragonfly共同优化了模型分发效率。
            homepage_url: https://openyurt.io/
            project: incubating
            repo_url: https://github.com/openyurtio/openyurt
            logo: openyurt.svg
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
            extra:
              lfx_slug: openyurt
              accepted: '2020-09-08'
              incubating: '2025-01-10'
              annual_review_url: https://github.com/cncf/toc/pull/1068
              annual_review_date: '2023-06-02'
              dev_stats_url: https://openyurt.devstats.cncf.io/
              clomonitor_name: openyurt
      - name: Container Registry
        items:
          - item:
            name: 12-迈向具身智能规模化：智元机器人基于 OpenYurt 的云边协同实践-蒋晨曦（智元机器人）、唐炳昌（阿里云）.md -
              Dragonfly
            description: >-
              面向运维工程师、架构师和技术管理者。该演讲主要介绍了智元机器人基于OpenYurt的云边协同实践，属于Provisioning分类中的基础设施自动化配置和管理。演讲中，OpenYurt作为云边协同基础平台，与Dragonfly共同优化了模型分发效率。
            homepage_url: https://d7y.io/
            project: graduated
            repo_url: https://github.com/dragonflyoss/dragonfly
            logo: dragonfly.svg
            twitter: https://twitter.com/dragonfly_oss
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: d7y
              accepted: '2018-11-13'
              incubating: '2020-04-09'
              graduated: '2025-10-28'
              dev_stats_url: https://dragonfly.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#dragonfly-logos
              mailing_list_url: https://groups.google.com/g/dragonfly-developers
              slack_url: https://cloud-native.slack.com/messages/dragonfly/
              summary_personas: SREs, Cloud Architects, Platform Engineers, 
                DevOps Engineer, DevOps practitioners, DevSecOps practitioners
              summary_tags: image acceleration, file distribution, images, OCI, 
                container, artefacts, registry, cloud native, p2p, dragonfly, 
                d7y, nydus
              summary_use_case: >-
                Provide efficient, stable, securefile distribution and image acceleration
                based on p2p technology to be the best practice and standard solution
                in cloud native architectures. It is widely used in the fields of
                image distribution, file distribution, log distribution, inference
                model distribution, etc.
              summary_business_use_case: >-
                Reduce back-to-source download traffic and back-to-source requests,
                improve node idle bandwidth utilization. Solve the problem of insufficient
                bandwidth or overload to download resources when accessing centralized
                container registry or file service in large-scale Kubernetes cluster.
                Based on P2P technology, reduce the bandwidth and load of container
                registry or file service, and accelerate the download speed of images
                or files.
              summary_release_rate: Major release every 2 months.
              summary_integrations: >-
                Harbor, Docker, Containerd, CRI-O, Podman, ORAS, Nydus, eStargz, HDFS,
                AWS S3, Google GCS, Azure Blob, Aliyun OSS, HWC OBS, TensorFlow Serving,
                Triton Server, TorchServe
              summary_intro_url: https://www.youtube.com/watch?v=TsECBaAvm-g
              clomonitor_name: dragonfly
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
      - name: Security & Compliance
        items: []
      - name: Key Management
        items: []
  - category:
    name: Runtime
    subcategories:
      - name: Cloud Native Storage
        items: []
      - name: Container Runtime
        items:
          - item:
            name: 10-基于Karmada + JuiceFS 的 AI 
              全球推理引擎管理实践-汪小康（GMICloud）、李少杰（JuiceFS）.md - Virtual Kubelet
            homepage_url: https://virtual-kubelet.io/
            project: sandbox
            repo_url: https://github.com/virtual-kubelet/virtual-kubelet
            logo: virtual-kubelet.svg
            twitter: https://twitter.com/virtualkubelet
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            second_path:
              - case / true
              - deep / 4
              - audience / 架构师
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: virtualkubelet
              accepted: '2018-12-04'
              dev_stats_url: https://virtualkubelet.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#virtual-kubelet-logos
              clomonitor_name: virtual-kubelet
              annual_review_url: https://github.com/cncf/toc/pull/1077
              annual_review_date: '2023-06-08'
            description: 
              面向架构师和技术管理者。该演讲介绍了基于Karmada和JuiceFS的全球AI推理引擎架构，其中Virtual 
              Kubelet作为Runtime层的关键组件，用于抽象和管理异构计算资源。演讲中，Virtual 
              Kubelet与Karmada协同，实现了多云资源的统一调度和管理。
      - name: Cloud Native Network
        items:
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - Cilium
            homepage_url: https://cilium.io/
            project: graduated
            repo_url: https://github.com/cilium/cilium
            url_for_bestpractices: 
              https://bestpractices.coreinfrastructure.org/en/projects/1269
            logo: cilium.svg
            twitter: https://twitter.com/ciliumproject
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: cilium
              accepted: '2021-10-13'
              incubating: '2021-10-13'
              graduated: '2023-10-11'
              dev_stats_url: https://cilium.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/incubating.md#cilium-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/cilium
              slack_url: https://cilium.herokuapp.com/
              linkedin_url: https://www.linkedin.com/company/cilium/
              summary_personas: Platform engineers, Network teams, SRE, DevOps 
                Engineers, Security teams
              summary_tags: CNI, Networking, Load Balancing, Service Mesh, 
                Observability, Runtime Security, eBPF, Cluster Mesh, Multicloud
              summary_use_case: >-
                Cilium is a networking, observability, and security platform based
                on eBPF. As a CNI, it provides a flat Layer 3 network, even across
                clusters. Cilium enforces network policies on L3-L7 using an identity
                based security model.


                Cilium implements distributed load balancing between pods and to external
                services by replacing kube-proxy. It also has advanced functionality
                like ingress and egress gateway, bandwidth management, service mesh,
                and deep network and security observability through Hubble and Tetragon.
              summary_business_use_case: >-
                Cilium reduces the operational complexity and cost of running highly
                scalable and dynamic cloud native environments. By leveraging eBPF,
                Cilium accelerates and simplifies service connectivity, makes systems
                observable for easier monitoring and debugging, and provides deep
                security observability and runtime enforcement.
              summary_release_rate: 2-3 major releases a year, plus minor and 
                point releases for bug fixes and security updates
              summary_integration: Kubernetes, Prometheus, OpenTelemetry, Envoy,
                Istio, Grafana, cert-manager, SPIFFE
              clomonitor_name: cilium
              audits:
                - date: '2023-02-13'
                  type: security
                  url: 
                    https://github.com/cilium/cilium.io/blob/main/Security-Reports/CiliumSecurityAudit2022.pdf
                  vendor: Ada Logics
                - date: '2023-02-13'
                  type: fuzzing
                  url: 
                    https://github.com/cilium/cilium.io/blob/main/Security-Reports/CiliumFuzzingAudit2022.pdf
                  vendor: Ada Logics
            description: 
              面向运维工程师和架构师。该演讲主要探讨超大规模集群和多集群场景下的网络挑战与解决方案，属于Runtime分类中的容器网络领域。演讲中，Cilium提供高性能eBPF数据平面，Submariner专注于跨集群L3连接，两者共同解决大规模集群的网络扩展问题。
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - 
              Submariner
            description: 
              面向运维工程师和架构师。该演讲主要探讨超大规模集群和多集群场景下的网络挑战与解决方案，属于Runtime分类中的容器网络领域。演讲中，Cilium提供高性能eBPF数据平面，Submariner专注于跨集群L3连接，两者共同解决大规模集群的网络扩展问题。
            homepage_url: https://submariner.io
            project: sandbox
            repo_url: https://github.com/submariner-io/submariner
            logo: submariner.svg
            twitter: https://twitter.com/submarinerio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: submariner
              accepted: '2021-04-28'
              clomonitor_name: submariner
              annual_review_url: https://github.com/cncf/toc/pull/1115
              annual_review_date: '2023-07-11'
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
  - category:
    name: Orchestration & Management
    subcategories:
      - name: Scheduling & Orchestration
        items:
          - item:
            name: 06-从 Device Plugin 到 DRA：GPU 调度范式升级与 HAMi DRA 
              实践-王纪飞（密瓜智能），James Deng（第四范式）.md - hami
            description: 面向开发工程师和运维工程师。该演讲聚焦于Kubernetes DRA（Dynamic Resource 
              Allocation）机制在GPU调度中的应用，属于编排与管理分类。演讲以HAMi项目为核心，展示了DRA如何替代传统Device 
              Plugin实现更精细的GPU资源管理。
            homepage_url: https://project-hami.github.io/HAMi/
            project: sandbox
            repo_url: https://github.com/Project-HAMi/HAMi
            logo: hami.svg
            open_source: true
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: hami
              accepted: '2024-08-21'
              clomonitor_name: hami
          - item:
            name: 04-云原生架构下分布式大模型推理的编排与调度实践-孙大光（华为）、鲁卫军（华为）.md - Volcano
            homepage_url: https://volcano.sh/
            project: incubating
            repo_url: https://github.com/volcano-sh/volcano
            logo: volcano.svg
            twitter: https://twitter.com/volcano_sh
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
            extra:
              lfx_slug: Volcano
              accepted: '2020-04-09'
              dev_stats_url: https://volcano.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#volcano
              incubating: '2022-03-21'
              clomonitor_name: volcano
              ai: true
            description: 
              面向架构师和运维工程师。该演讲聚焦大模型推理在K8s上的编排与调度优化，属于编排与管理分类。演讲中，Kthena作为编排框架，依赖Volcano提供拓扑感知和Gang调度能力。
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - 
              Kubernetes
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
            homepage_url: https://kubernetes.io/
            project: graduated
            repo_url: https://github.com/kubernetes/kubernetes
            logo: kubernetes.svg
            twitter: https://twitter.com/kubernetesio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: k8s
              accepted: '2016-03-10'
              incubating: '2016-03-10'
              graduated: '2018-03-06'
              dev_stats_url: https://k8s.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#kubernetes-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/kubernetes
              blog_url: http://blog.kubernetes.io/
              mailing_list_url: 
                https://groups.google.com/forum/#!forum/kubernetes-dev
              slack_url: http://slack.k8s.io/
              youtube_url: 
                https://www.youtube.com/channel/UCZ2bu0qutTOM0tHYa_jkIwg
              clomonitor_name: kubernetes
              audits:
                - date: '2019-08-06'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2019/findings
                  vendor: Trail of Bits and Atredis Partners
                - date: '2023-04-19'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2021-2022/findings
                  vendor: NCC Group
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - Karmada
            homepage_url: https://karmada.io/
            project: incubating
            repo_url: https://github.com/karmada-io/karmada
            logo: karmada.svg
            twitter: https://x.com/karmada_io
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: karmada
              accepted: '2021-09-14'
              incubating: '2023-12-12'
              annual_review_url: https://github.com/cncf/toc/pull/954
              annual_review_date: '2022-10-29'
              dev_stats_url: https://karmada.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#karmada-logos
              mailing_list_url: https://groups.google.com/forum/#!forum/karmada
              slack_url: https://cloud-native.slack.com/messages/karmada
              chat_channel: '#karmada'
              clomonitor_name: karmada
              summary_personas: Cloud Architects, Platform Engineers, DevOps 
                Engineer, DevOps practitioners, DevSecOps practitioners
              summary_tags: multi-cloud, hybrid cloud
              summary_use_case: >-
                Karmada (Kubernetes Armada) is a Kubernetes management system that
                enables you to run your cloud-native applications across multiple
                Kubernetes clusters and clouds, with no changes to your applications.
                By speaking Kubernetes-native APIs and providing advanced scheduling  capabilities,
                Karmada enables truly open, multi-cloud Kubernetes. Karmada aims to
                provide turnkey automation for multi-cluster application management
                in multi-cloud and hybrid cloud scenarios, with key  features such
                as centralized multi-cloud management, high availability, failure
                recovery, and traffic scheduling.
              summary_business_use_case: >-
                Managing workloads across diverse enterprise environments is complex.
                Karmada, a multi-cluster orchestration system, provides a unified  control
                plane for Kubernetes clusters. It enables seamless workload deployment,
                scaling, and management across on-premises and multi-cloud  setups,
                addressing operational challenges, reducing vendor lock-in, and enhancing
                disaster recovery and infrastructure efficiency.
              summary_release_rate: Every 3 months
              summary_integrations: Volcano, Kubeflow, Kyverno, Pytorch, 
                TensorFlow, Flink, Spark, OpenKruise, Grafana, Prometheus, Argo,
                Flux, Istio, Filebeat, Submariner, Velero, etc.
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - 
              Clusternet
            homepage_url: https://clusternet.io
            project: sandbox
            repo_url: https://github.com/clusternet/clusternet
            logo: clusternet.svg
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: clusternet
              accepted: '2023-03-07'
              dev_stats_url: https://clusternet.devstats.cncf.io/
              slack_url: https://cloud-native.slack.com/messages/clusternet
              clomonitor_name: clusternet
            description: '面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration
              & Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。'
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - KEDA
            homepage_url: https://keda.sh/
            project: graduated
            repo_url: https://github.com/kedacore/keda
            logo: keda.svg
            twitter: https://twitter.com/kedaorg
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: KEDA
              accepted: '2020-03-12'
              incubating: '2021-08-18'
              graduated: '2023-08-22'
              dev_stats_url: https://keda.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#keda-logos
              blog_url: https://keda.sh/blog
              slack_url: https://kubernetes.slack.com/archives/CKZJ36A5D
              youtube_url: 
                https://www.youtube.com/playlist?list=PLvjRi5R9GQfASIcL4MLTfg2ZOoBdIi5Kq
              clomonitor_name: keda
              audits:
                - date: '2023-02-02'
                  type: security
                  url: 
                    https://github.com/trailofbits/publications/blob/master/reviews/2023-01-keda-securityreview.pdf
                  vendor: Trail of Bits
              summary_release_rate: Every 4 months
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - 
              Clusterpedia
            description: >-
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
            homepage_url: https://clusterpedia.io
            project: sandbox
            repo_url: https://github.com/clusterpedia-io/clusterpedia
            logo: clusterpedia.svg
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: clusterpedia
              accepted: '2022-06-17'
              dev_stats_url: https://clusterpedia.devstats.cncf.io/
              slack_url: https://cloud-native.slack.com/messages/clusterpedia
              clomonitor_name: clusterpedia
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - Volcano
            homepage_url: https://volcano.sh/
            project: incubating
            repo_url: https://github.com/volcano-sh/volcano
            logo: volcano.svg
            twitter: https://twitter.com/volcano_sh
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: Volcano
              accepted: '2020-04-09'
              dev_stats_url: https://volcano.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#volcano
              incubating: '2022-03-21'
              clomonitor_name: volcano
              ai: true
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
          - item:
            name: 02-Pythonic Argo_ Building Maintainable and Dynamic ML 
              Pipelines as Code-Shuangkun Tian，Lei Hou（Alicloud）.md - Kubeflow
            description: 
              面向开发工程师和架构师。该演讲主要介绍使用Python构建可维护的ML工作流，属于编排与管理分类。演讲中，Argo 
              Workflows作为底层编排引擎，与Kubeflow Pipelines等MLOps框架协同，实现ML管线的自动化。
            homepage_url: https://kubeflow.org
            project: incubating
            repo_url: https://github.com/kubeflow/kubeflow
            logo: kubeflow.svg
            twitter: https://twitter.com/kubeflow
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: kubeflow
              accepted: '2023-07-25'
              incubating: '2023-07-25'
              blog_url: https://blog.kubeflow.org
              slack_url: http://slack.cncf.io/
              mailing_list_url: https://groups.google.com/g/kubeflow-discuss
              linkedin_url: https://www.linkedin.com/company/kubeflow
              dev_stats_url: https://kubeflow.devstats.cncf.io/
              clomonitor_name: kubeflow
              ai: true
              other_links:
                - name: Twitter
                  url: https://x.com/kubeflow
                - name: Bluesky
                  url: https://bsky.app/profile/kubefloworg.bsky.social
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
          - item:
            name: 10-基于Karmada + JuiceFS 的 AI 
              全球推理引擎管理实践-汪小康（GMICloud）、李少杰（JuiceFS）.md - Karmada
            homepage_url: https://karmada.io/
            project: incubating
            repo_url: https://github.com/karmada-io/karmada
            logo: karmada.svg
            twitter: https://x.com/karmada_io
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
            extra:
              lfx_slug: karmada
              accepted: '2021-09-14'
              incubating: '2023-12-12'
              annual_review_url: https://github.com/cncf/toc/pull/954
              annual_review_date: '2022-10-29'
              dev_stats_url: https://karmada.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/sandbox.md#karmada-logos
              mailing_list_url: https://groups.google.com/forum/#!forum/karmada
              slack_url: https://cloud-native.slack.com/messages/karmada
              chat_channel: '#karmada'
              clomonitor_name: karmada
              summary_personas: Cloud Architects, Platform Engineers, DevOps 
                Engineer, DevOps practitioners, DevSecOps practitioners
              summary_tags: multi-cloud, hybrid cloud
              summary_use_case: >-
                Karmada (Kubernetes Armada) is a Kubernetes management system that
                enables you to run your cloud-native applications across multiple
                Kubernetes clusters and clouds, with no changes to your applications.
                By speaking Kubernetes-native APIs and providing advanced scheduling  capabilities,
                Karmada enables truly open, multi-cloud Kubernetes. Karmada aims to
                provide turnkey automation for multi-cluster application management
                in multi-cloud and hybrid cloud scenarios, with key  features such
                as centralized multi-cloud management, high availability, failure
                recovery, and traffic scheduling.
              summary_business_use_case: >-
                Managing workloads across diverse enterprise environments is complex.
                Karmada, a multi-cluster orchestration system, provides a unified  control
                plane for Kubernetes clusters. It enables seamless workload deployment,
                scaling, and management across on-premises and multi-cloud  setups,
                addressing operational challenges, reducing vendor lock-in, and enhancing
                disaster recovery and infrastructure efficiency.
              summary_release_rate: Every 3 months
              summary_integrations: Volcano, Kubeflow, Kyverno, Pytorch, 
                TensorFlow, Flink, Spark, OpenKruise, Grafana, Prometheus, Argo,
                Flux, Istio, Filebeat, Submariner, Velero, etc.
            description: 
              面向运维工程师、架构师和技术管理者。该演讲主要介绍了基于Karmada的多集群管理调度平台实践，属于编排与管理分类。演讲中，Karmada作为核心编排框架，与自研的BRS抽象层和调度系统协同，构建了全球异构资源统一管理平台。
          - item:
            name: 03-Valkey on Kubernetes_ High-Performance, Low-Latency 
              Deployments for Scalable Clusters.md - Kubernetes
            description: 
              面向运维工程师和架构师。该演讲主要介绍了Valkey在Kubernetes上的部署和管理实践，属于编排与管理分类。演讲中，Kubernetes作为核心编排平台，结合StatefulSets、Operators等原生资源来管理Valkey集群。
            homepage_url: https://kubernetes.io/
            project: graduated
            repo_url: https://github.com/kubernetes/kubernetes
            logo: kubernetes.svg
            twitter: https://twitter.com/kubernetesio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
            extra:
              lfx_slug: k8s
              accepted: '2016-03-10'
              incubating: '2016-03-10'
              graduated: '2018-03-06'
              dev_stats_url: https://k8s.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#kubernetes-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/kubernetes
              blog_url: http://blog.kubernetes.io/
              mailing_list_url: 
                https://groups.google.com/forum/#!forum/kubernetes-dev
              slack_url: http://slack.k8s.io/
              youtube_url: 
                https://www.youtube.com/channel/UCZ2bu0qutTOM0tHYa_jkIwg
              clomonitor_name: kubernetes
              audits:
                - date: '2019-08-06'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2019/findings
                  vendor: Trail of Bits and Atredis Partners
                - date: '2023-04-19'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2021-2022/findings
                  vendor: NCC Group
          - item:
            name: 07-把吵闹邻居揪出来：Kubernetes 混部场景下的干扰检测与 SLO 治理实践-曹阳（京东）.md - 
              Kubernetes
            description: 
              面向运维工程师和架构师。该演讲聚焦Kubernetes混部场景下的干扰检测与SLO治理，属于编排与管理分类。演讲中，Kubernetes作为基础编排平台，Koordinator（或其类似技术）用于增强混部调度与QoS保障。
            homepage_url: https://kubernetes.io/
            project: graduated
            repo_url: https://github.com/kubernetes/kubernetes
            logo: kubernetes.svg
            twitter: https://twitter.com/kubernetesio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
            extra:
              lfx_slug: k8s
              accepted: '2016-03-10'
              incubating: '2016-03-10'
              graduated: '2018-03-06'
              dev_stats_url: https://k8s.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#kubernetes-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/kubernetes
              blog_url: http://blog.kubernetes.io/
              mailing_list_url: 
                https://groups.google.com/forum/#!forum/kubernetes-dev
              slack_url: http://slack.k8s.io/
              youtube_url: 
                https://www.youtube.com/channel/UCZ2bu0qutTOM0tHYa_jkIwg
              clomonitor_name: kubernetes
              audits:
                - date: '2019-08-06'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2019/findings
                  vendor: Trail of Bits and Atredis Partners
                - date: '2023-04-19'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2021-2022/findings
                  vendor: NCC Group
          - item:
            name: 07-把吵闹邻居揪出来：Kubernetes 混部场景下的干扰检测与 SLO 治理实践-曹阳（京东）.md - 
              Koordinator
            description: 
              面向运维工程师和架构师。该演讲聚焦Kubernetes混部场景下的干扰检测与SLO治理，属于编排与管理分类。演讲中，Kubernetes作为基础编排平台，Koordinator（或其类似技术）用于增强混部调度与QoS保障。
            homepage_url: https://koordinator.sh
            repo_url: https://github.com/koordinator-sh/koordinator
            logo: Koordinator.svg
            crunchbase: https://www.crunchbase.com/organization/alibaba-cloud
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
            project: sandbox
            extra:
              lfx_slug: koordinator
              accepted: '2024-04-16'
              clomonitor_name: koordinator
          - item:
            name: 08-Gang Scheduling in Kubernetes - What’s Happening and What 
              Comes Next-范宝发（DaoCloud）.md - Kubernetes
            description: 面向开发工程师和运维工程师。该演讲深入探讨Kubernetes中的Gang 
              Scheduling机制，属于编排与管理分类的核心调度功能。演讲聚焦Kubernetes原生调度器增强，通过Workload 
              API和PodGroup实现批量Pod协同调度。
            homepage_url: https://kubernetes.io/
            project: graduated
            repo_url: https://github.com/kubernetes/kubernetes
            logo: kubernetes.svg
            twitter: https://twitter.com/kubernetesio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            second_path:
              - case / false
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
            extra:
              lfx_slug: k8s
              accepted: '2016-03-10'
              incubating: '2016-03-10'
              graduated: '2018-03-06'
              dev_stats_url: https://k8s.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#kubernetes-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/kubernetes
              blog_url: http://blog.kubernetes.io/
              mailing_list_url: 
                https://groups.google.com/forum/#!forum/kubernetes-dev
              slack_url: http://slack.k8s.io/
              youtube_url: 
                https://www.youtube.com/channel/UCZ2bu0qutTOM0tHYa_jkIwg
              clomonitor_name: kubernetes
              audits:
                - date: '2019-08-06'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2019/findings
                  vendor: Trail of Bits and Atredis Partners
                - date: '2023-04-19'
                  type: security
                  url: 
                    https://github.com/kubernetes/sig-security/tree/main/sig-security-external-audit/security-audit-2021-2022/findings
                  vendor: NCC Group
      - name: Coordination & Service Discovery
        items:
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - etcd
            homepage_url: https://etcd.io/
            project: graduated
            repo_url: https://github.com/etcd-io/etcd
            logo: etcd.svg
            twitter: https://twitter.com/etcdio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: etcd
              accepted: '2018-12-11'
              incubating: '2018-12-11'
              graduated: '2020-11-24'
              dev_stats_url: https://etcd.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#etcd-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/etcd
              mailing_list_url: 
                https://groups.google.com/forum/?hl=en#!forum/etcd-dev
              slack_url: http://slack.k8s.io/
              chat_channel: '#etcd'
              clomonitor_name: etcd
              audits:
                - date: '2020-08-05'
                  type: security
                  url: 
                    https://github.com/etcd-io/etcd/blob/master/security/SECURITY_AUDIT.pdf
                  vendor: Trail of Bits
                - date: '2022-03-11'
                  type: fuzzing
                  url: 
                    https://github.com/etcd-io/etcd/blob/main/security/FUZZING_AUDIT_2022.PDF
                  vendor: ADA Logics
              summary_personas: Architects, Platform Engineers, SREs
              summary_tags: distributed, key-value, consistent, datastore, 
                reliability
              summary_use_case: >-
                Etcd is a distributed, reliable key-value store for the most critical
                data of a distributed system. By using etcd, developers can ensure
                that their applications have access to up-to-date configuration data,
                even as they scale up or down, and can maintain consistency, fault
                tolerance and coordination across multiple instances of the application.
              summary_business_use_case: >-
                Etcd can be used by businesses to manage the configuration data and
                coordination needs of their applications, ensuring consistent and
                reliable performance at scale.
              summary_release_rate: Major or Minor release approximately every 2
                years, and patch release every 1-3 months
              summary_integrations: >-
                Due to etcd's flexible and extensible architecture it has been integrated
                as a data store for many projects such as Kubernetes, CoreDNS and
                Hashicorp Vault.
              summary_intro_url: https://etcd.io/docs/v3.6/learning/
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - CoreDNS
            homepage_url: https://coredns.io/
            project: graduated
            repo_url: https://github.com/coredns/coredns
            logo: core-dns.svg
            twitter: https://twitter.com/corednsio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: coredns
              accepted: '2017-02-27'
              incubating: '2018-02-26'
              graduated: '2019-01-24'
              dev_stats_url: https://coredns.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#coredns-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/coredns
              blog_url: https://coredns.io/blog/
              mailing_list_url: 
                https://groups.google.com/forum/#!forum/coredns-discuss
              slack_url: https://cloud-native.slack.com/messages/coredns/
              youtube_url: 
                https://www.youtube.com/channel/UCbWRJZxiaQ8twm6sh7UymoQ
              clomonitor_name: core-dns
              audits:
                - date: '2018-02-03'
                  type: security
                  url: https://coredns.io/assets/DNS-01-report.pdf
                  vendor: Cure53
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
          - item:
            name: 11-Etcd大规模集群中性能挑战与内核级突破-汪扬（蚂蚁）.md - etcd
            homepage_url: https://etcd.io/
            project: graduated
            repo_url: https://github.com/etcd-io/etcd
            logo: etcd.svg
            twitter: https://twitter.com/etcdio
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: etcd
              accepted: '2018-12-11'
              incubating: '2018-12-11'
              graduated: '2020-11-24'
              dev_stats_url: https://etcd.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#etcd-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/etcd
              mailing_list_url: 
                https://groups.google.com/forum/?hl=en#!forum/etcd-dev
              slack_url: http://slack.k8s.io/
              chat_channel: '#etcd'
              clomonitor_name: etcd
              audits:
                - date: '2020-08-05'
                  type: security
                  url: 
                    https://github.com/etcd-io/etcd/blob/master/security/SECURITY_AUDIT.pdf
                  vendor: Trail of Bits
                - date: '2022-03-11'
                  type: fuzzing
                  url: 
                    https://github.com/etcd-io/etcd/blob/main/security/FUZZING_AUDIT_2022.PDF
                  vendor: ADA Logics
              summary_personas: Architects, Platform Engineers, SREs
              summary_tags: distributed, key-value, consistent, datastore, 
                reliability
              summary_use_case: >-
                Etcd is a distributed, reliable key-value store for the most critical
                data of a distributed system. By using etcd, developers can ensure
                that their applications have access to up-to-date configuration data,
                even as they scale up or down, and can maintain consistency, fault
                tolerance and coordination across multiple instances of the application.
              summary_business_use_case: >-
                Etcd can be used by businesses to manage the configuration data and
                coordination needs of their applications, ensuring consistent and
                reliable performance at scale.
              summary_release_rate: Major or Minor release approximately every 2
                years, and patch release every 1-3 months
              summary_integrations: >-
                Due to etcd's flexible and extensible architecture it has been integrated
                as a data store for many projects such as Kubernetes, CoreDNS and
                Hashicorp Vault.
              summary_intro_url: https://etcd.io/docs/v3.6/learning/
            description: 
              面向运维工程师和架构师。该演讲深入探讨etcd在大规模AI集群中的性能优化，属于编排与管理分类中的核心存储组件。演讲聚焦etcd自身的内核级优化，未涉及与其他项目的集成关系。
            second_path:
              - case / true
              - deep / 5
              - audience / 混合
              - diffcult / 5
              - deployment / 3
      - name: Remote Procedure Call
        items: []
      - name: Service Proxy
        items: []
      - name: API Gateway
        items: []
      - name: Service Mesh
        items:
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - Istio
            description: 
              面向运维工程师、架构师和技术管理者。该演讲深入探讨了超大规模集群和多集群场景下的编排与管理挑战，属于Orchestration & 
              Management分类。演讲中，Kubernetes作为核心编排平台，与etcd、Karmada、Clusternet等项目共同构建了大规模集群的管理体系。
            homepage_url: https://istio.io/
            project: graduated
            repo_url: https://github.com/istio/istio
            logo: istio.svg
            twitter: https://twitter.com/IstioMesh
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            allow_duplicate_repo: true
            extra:
              lfx_slug: istio
              accepted: '2022-09-30'
              incubating: '2022-09-30'
              graduated: '2023-07-12'
              dev_stats_url: https://istio.devstats.cncf.io/
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/istio
              blog_url: https://istio.io/blog/
              slack_url: https://slack.istio.io/
              summary_business_use_case: Security, Traffic Management, 
                Observability, Policy Enforcement.
              summary_integrations: 
                https://istio.io/latest/docs/ops/integrations/
              summary_intro_url: https://www.youtube.com/watch?v=hkR1M6qwpnw
              summary_personas: SREs, DevOps Engineers, Architects, Platform 
                Engineers
              summary_release_rate: Every 3 months
              summary_tags: observability, Service Mesh, mTLS, traffic 
                management, multi-cluster, load balancing, policy, security, 
                extensibility
              summary_use_case: >-
                Istio addresses the challenges developers and operators face with
                a distributed or microservices architecture. Whether you're building
                from scratch or migrating existing applications to cloud native, Istio
                can help. Read more at: https://istio.io/latest/about/solutions/
              youtube_url: https://www.youtube.com/c/Istio
              clomonitor_name: istio
              audits:
                - date: '2023-01-30'
                  type: security
                  url: 
                    https://istio.io/latest/blog/2023/ada-logics-security-assessment/Istio%20audit%20report%20-%20ADA%20Logics%20-%202023-01-30%20-%20v1.0.pdf
                  vendor: ADA Logics
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 3
  - category:
    name: App Definition and Development
    subcategories:
      - name: Database
        items: []
      - name: Streaming & Messaging
        items: []
      - name: Application Definition & Image Build
        items: []
      - name: Continuous Integration & Delivery
        items: []
  - category:
    name: Platform
    subcategories:
      - name: Certified Kubernetes - Distribution
        items: []
      - name: Certified Kubernetes - Hosted
        items: []
      - name: Certified Kubernetes - Installer
        items: []
      - name: Certified Kubernetes - AI Platform
        items: []
      - name: PaaS/Container Service
        items: []
  - category:
    name: Serverless
    subcategories:
      - name: Security
        items: []
      - name: Tools
        items: []
      - name: Framework
        items: []
      - name: Hosted Platform
        items: []
      - name: Installable Platform
        items: []
  - category:
    name: Observability and Analysis
    subcategories:
      - name: Feature Flagging
        items: []
      - name: Chaos Engineering
        items: []
      - name: Continuous Optimization
        items: []
      - name: Observability
        items:
          - item:
            name: 07-把吵闹邻居揪出来：Kubernetes 混部场景下的干扰检测与 SLO 治理实践-曹阳（京东）.md - 
              Prometheus
            homepage_url: https://prometheus.io/
            project: graduated
            repo_url: https://github.com/prometheus/prometheus
            logo: prometheus.svg
            twitter: https://twitter.com/PrometheusIO
            crunchbase: 
              https://www.crunchbase.com/organization/cloud-native-computing-foundation
            extra:
              lfx_slug: prometheus_del
              accepted: '2016-05-09'
              incubating: '2016-05-09'
              graduated: '2018-08-09'
              dev_stats_url: https://prometheus.devstats.cncf.io/
              artwork_url: 
                https://github.com/cncf/artwork/blob/master/examples/graduated.md#prometheus-logos
              stack_overflow_url: 
                https://stackoverflow.com/questions/tagged/prometheus
              blog_url: https://prometheus.io/blog/
              mailing_list_url: https://prometheus.io/community/
              youtube_url: 
                https://www.youtube.com/channel/UC4pLFely0-Odea4B2NL1nWA
              slack_url: https://libera.chat/
              chat_channel: '#prometheus'
              summary_personas: Cluster Administrators, Developers, SREs, 
                Platform Engineers, Network Engineers
              summary_tags: monitoring, alerting, observability, instrumentation
              summary_use_case: metrics-based monitoring and alerting
              summary_release_rate: every 6 weeks for prometheus/prometheus
              summary_integrations: >-
                See [exporters](https://prometheus.io/docs/instrumenting/exporters/)
                and [clientlibs](https://prometheus.io/docs/instrumenting/clientlibs/)
                plus many service discoveries.
              clomonitor_name: prometheus
              audits:
                - date: '2018-06-11'
                  type: security
                  url: 
                    https://prometheus.io/assets/downloads/2018-06-11--cure53_security_audit.pdf
                  vendor: Cure53
                - date: '2020-07-21'
                  type: security
                  url: 
                    https://prometheus.io/assets/downloads/2020-07-21--cure53_security_audit_node_exporter.pdf
                  vendor: Cure53
            description: 
              面向运维工程师和架构师。该演讲聚焦Kubernetes混部场景下的干扰检测与SLO治理，属于可观测性分类，通过Prometheus等工具采集业务、系统及微架构指标实现全栈可观测。
            second_path:
              - case / true
              - deep / 4
              - audience / 混合
              - diffcult / 4
              - deployment / 4
  - category:
    name: Special
    subcategories:
      - name: Kubernetes Certified Service Provider
        items: []
      - name: Kubernetes and Cloud Native Training Partner
        items: []
      - name: Certified CNFs
        items: []
  - category:
    name: CNCF Members
    subcategories:
      - name: Platinum
        items: []
      - name: Gold
        items: []
      - name: Silver
        items: []
      - name: Academic
        items: []
      - name: Nonprofit
        items: []
      - name: End User Supporter and Contributor
        items: []
  - category:
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    subcategories:
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        items: []
      - name: Runtimes
        items: []
      - name: Application Frameworks
        items: []
      - name: Edge/Bare metal
        items: []
      - name: AI/Machine Learning
        items: []
      - name: Embedded Functions
        items: []
      - name: Tooling
        items: []
      - name: Orchestration & Management
        items: []
      - name: Hosted Platforms
        items: []
      - name: Decentralized Platforms
        items: []
      - name: Debugging & Observability
        items: []
      - name: Packaging, Registries & Application Delivery
        items: []
  - category:
    name: CNAI
    subcategories:
      - name: General Orchestration
        items:
          - item:
            name: 01-超大集群还是多集群？如何定位关键瓶颈-徐俊杰（DaoCloud），郭奇峰（DaoCloud）.md - Kueue
            description: 
              面向架构师和技术管理者。该演讲探讨了超大规模集群和多集群架构的挑战与解决方案，属于云原生应用基础设施分类。演讲中，KubeBrain作为etcd替代方案，与Clusterpedia、Submariner、Cilium等项目共同构建了大规模集群的优化体系。
            repo_url: https://github.com/kubernetes-sigs/kueue
            logo: ./kueue.svg
            unnamed_organization: true
            homepage_url: https://kueue.sigs.k8s.io/
            second_path:
              - case / true
              - deep / 4
              - audience / 架构师
              - diffcult / 4
              - deployment / 3
      - name: ML Serving
        items: []
      - name: CI/CD - Delivery
        items: []
      - name: Data Science
        items: []
      - name: Distributed Training
        items: []
      - name: Workload Observability
        items: []
      - name: Model/LLM Observability
        items: []
      - name: AutoML
        items: []
      - name: Vector Databases
        items: []
      - name: Data Architecture
        items: []
      - name: Governance, Policy & Security
        items: []
      - name: Open Enterprise AI Blueprints
        items: []
  - category:
    name: case
    subcategories:
      - name: Have
        items: []
      - name: Not
        items: []
  - category:
    name: deep
    subcategories:
      - name: "1 - 入门"
        items: []
      - name: "2 - 基础"
        items: []
      - name: "3 - 中级"
        items: []
      - name: "4 - 高级"
        items: []
      - name: "5 - 深度"
        items: []
  - category:
    name: audience
    subcategories:
      - name: "运维工程师"
        items: []
      - name: "开发工程师"
        items: []
      - name: "架构师"
        items: []
      - name: "技术管理者"
        items: []
      - name: "混合"
        items: []
  - category:
    name: diffcult
    subcategories:
      - name: "1 - 简单"
        items: []
      - name: "2 - 较低"
        items: []
      - name: "3 - 中等"
        items: []
      - name: "4 - 较高"
        items: []
      - name: "5 - 复杂"
        items: []
  - category:
    name: deployment
    subcategories:
      - name: "1 - 理论性强"
        items: []
      - name: "2 - 较难落地"
        items: []
      - name: "3 - 一般"
        items: []
      - name: "4 - 易实践"
        items: []
      - name: "5 - 即学即用"
        items: []