Huawei Cloud ModelArts Skillv1.0.1

Use the list_resource_overview action to quickly assess the current utilization of GPU, CPU, memory, and storage across the ModelArts workspace. This enables IT administrators to plan capacity upgrades or reallocate resources during peak demand.

Combine list_training_jobs and create_training_job to monitor active training runs and trigger new jobs when previous ones complete. This is useful for iterative model training workflows in machine learning pipelines.

Use list_models to track all trained model versions stored in ModelArts. This helps data scientists manage model registries and decide which version to promote to production for inference.

Regularly invoke list_services to check the status, endpoint health, and load of deployed inference services. Operations teams can detect failures or scale issues early and respond proactively.

Use list_notebooks to audit all running Jupyter Notebook instances, including their attached resources and users. This aids in cost control and security compliance by identifying idle or unauthorized notebooks.

Offer a subscription-based AI development platform where enterprises pay for managed access to ModelArts resources including training, hosting, and notebook environments. The skill automates resource monitoring and management, reducing operational overhead.

Provide consulting services to clients for building automated MLOps pipelines on Huawei Cloud. The skill enables rapid integration of training job creation and model management into CI/CD workflows, billed per project or retainer.

Create a service that takes customer training scripts and data, uses the skill to automatically create and manage training jobs, and returns the trained model. Revenue comes from per-training-job fees or compute-hour markup.