B.4 Solution Deployment

6.1 Deploy and integrate AI solutions, including models, pipelines, or intelligent applications, into production systems independently using MLOps pipelines or other suitable methods, such as cloud-based or edge computing environments.

Assessed by Deployed AI solution demonstration, integration scripts review, and architecture diagram evaluation, assessed for independent deployment and integration of models, pipelines, or intelligent applications into production systems using MLOps or other suitable methods.

6.2 Monitor AI solution performance, evaluate operational metrics, and troubleshoot issues affecting deployment in real-time or batch-processing environments.

Assessed by AI performance report review, monitoring dashboard evaluation, and troubleshooting log analysis, assessed for monitoring of AI solution performance, evaluation of operational metrics, and troubleshooting of deployment issues.

6.3 Implement updates or retraining processes, or modify system components, to maintain AI solution performance by leveraging automated pipelines or scheduled workflows.

Assessed by Updated/retrained AI model verification, workflow documentation review, and test result analysis, assessed for implementation of updates, retraining processes, or system modifications using automated pipelines or scheduled workflows.

6.4 Produce operational and lifecycle documentation to support handover and continued maintenance of AI solutions in enterprise or production settings.

Assessed by Operational manual evaluation, lifecycle documentation review, and handover report assessment, assessed for completeness and usefulness in supporting handover and continued maintenance of AI solutions in enterprise or production settings.

6.5 Apply ethical and security considerations when deploying and managing AI solutions, such as data privacy compliance, fairness, and bias mitigation practices.

Assessed by Bias/fairness report evaluation, data privacy compliance review, and security assessment evaluation, assessed for application of ethical and security considerations, including privacy compliance, fairness, and bias mitigation, in AI deployment and management.

3.1 Deploy and integrate AI solutions, including models, pipelines, or intelligent applications, into production systems independently using MLOps pipelines or other suitable methods, such as cloud-based or edge computing environments.

Assessed by Deployed AI solution demonstration, integration scripts review, and architecture diagram evaluation, assessed for independent deployment and integration of models, pipelines, or intelligent applications into production systems using MLOps or other suitable methods.

3.2 Monitor AI solution performance, evaluate operational metrics, and troubleshoot issues affecting deployment in real-time or batch-processing environments.

Assessed by AI performance report review, monitoring dashboard evaluation, and troubleshooting log analysis, assessed for monitoring of AI solution performance, evaluation of operational metrics, and troubleshooting of deployment issues.

3.3 Implement updates or retraining processes, or modify system components, to maintain AI solution performance by leveraging automated pipelines or scheduled workflows.

Assessed by Updated/retrained AI model verification, workflow documentation review, and test result analysis, assessed for implementation of updates, retraining processes, or system modifications using automated pipelines or scheduled workflows.

3.4 Produce operational and lifecycle documentation to support handover and continued maintenance of AI solutions in enterprise or production settings.

Assessed by Operational manual evaluation, lifecycle documentation review, and handover report assessment, assessed for completeness and usefulness in supporting handover and continued maintenance of AI solutions in enterprise or production settings.

3.5 Apply ethical and security considerations when deploying and managing AI solutions, such as data privacy compliance, fairness, and bias mitigation practices.

Assessed by Bias/fairness report evaluation, data privacy compliance review, and security assessment evaluation, assessed for application of ethical and security considerations, including privacy compliance, fairness, and bias mitigation, in AI deployment and management.

3.1 Deploy and integrate AI solutions, including models, pipelines, or intelligent applications, into production systems independently using MLOps pipelines or other suitable methods, such as cloud-based or edge computing environments.

Assessed by Deployed AI solution demonstration, integration scripts review, and architecture diagram evaluation, assessed for independent deployment and integration of models, pipelines, or intelligent applications into production systems using MLOps pipelines or other suitable methods.

3.2 Monitor AI solution performance, evaluate operational metrics, and troubleshoot issues affecting deployment in real-time or batch-processing environments.

Assessed by AI performance report review, monitoring dashboard evaluation, and troubleshooting log analysis, assessed for monitoring of AI solution performance, evaluation of operational metrics, and troubleshooting of deployment issues in real-time or batch-processing environments.

3.3 Implement updates or retraining processes, or modify system components, to maintain AI solution performance by leveraging automated pipelines or scheduled workflows.

Assessed by Updated/retrained AI model verification, workflow documentation review, and test result analysis, assessed for implementation of updates, retraining processes, or system modifications using automated pipelines or scheduled workflows to maintain AI solution performance.

3.4 Produce operational and lifecycle documentation to support handover and continued maintenance of AI solutions in enterprise or production settings.

Assessed by Operational manual evaluation, lifecycle documentation review, and handover report assessment, assessed for completeness and usefulness in supporting handover and continued maintenance of AI solutions in enterprise or production settings.

3.5 Apply ethical and security considerations when deploying and managing AI solutions, such as data privacy compliance, fairness, and bias mitigation practices.

Assessed by Bias/fairness report evaluation, data privacy compliance review, and security assessment evaluation, assessed for application of ethical and security considerations, including privacy compliance, fairness, and bias mitigation, in AI deployment and management.