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Operational maturity areas | GSA - IT Modernization Centers of Excellence

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  1. Operational maturity areas represent organizational functions that impact the implementation of AI capabilities. “While each area is treated as a discrete capability for maturity evaluation, they generally depend on one another. The operational maturity areas are:” PeopleOps: Recruit, develop, retain, and organize an AI-ready workforce.

    In Operational maturity criteria

  2. While each area is treated as a discrete capability for maturity evaluation, they generally depend on one another. The operational maturity areas are: “PeopleOps: Recruit, develop, retain, and organize an AI-ready workforce.” CloudOps: Provide and allocate storage, compute, and other resources in the cloud.

    In Operational maturity criteria

  3. DevOps: Deploy and manage software throughout development, test, and production environments. “DataOps: Maximize data discovery, access, and use throughout its lifecycle.” MLOps: Test, experiment, and deploy AI or ML models.

    In Operational maturity criteria

  4. PeopleOps: Recruit, develop, retain, and organize an AI-ready workforce. “CloudOps: Provide and allocate storage, compute, and other resources in the cloud.” SecOps: Ensure secure deployment of code, access to systems and data, and identity resolution across storage, compute, and data assets.

    In Operational maturity criteria

  5. CloudOps: Provide and allocate storage, compute, and other resources in the cloud. “SecOps: Ensure secure deployment of code, access to systems and data, and identity resolution across storage, compute, and data assets.” DevOps: Deploy and manage software throughout development, test, and production environments.

    In Operational maturity criteria

  6. SecOps: Ensure secure deployment of code, access to systems and data, and identity resolution across storage, compute, and data assets. “DevOps: Deploy and manage software throughout development, test, and production environments.” DataOps: Maximize data discovery, access, and use throughout its lifecycle.

    In Operational maturity criteria

  7. DataOps: Maximize data discovery, access, and use throughout its lifecycle. “MLOps: Test, experiment, and deploy AI or ML models.” AIOps: Identify and resource AI initiatives within the organization.

    In Operational maturity criteria

  8. MLOps: Test, experiment, and deploy AI or ML models. “AIOps: Identify and resource AI initiatives within the organization.” Each operational maturity area below is supported by related key activities and key questions to focus the conversation on the current organizational state.

    In Operational maturity criteria

  9. Create and secure development pipelines from local to cloud environments. “Contain and isolate dependencies for tools to effectively use and scale resources.” Reduce delivery and iteration timelines through development pipeline automation and full containerization.

    In Integration boundaries and handoffs

  10. Create and secure development pipelines from local to cloud environments. “Contain and isolate dependencies for tools to effectively use and scale resources.” Reduce delivery and iteration timelines through development pipeline automation and full containerization.

    In Outbound stack architecture

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