implementing-mlops
byancoleman· 58 skills
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
How to install implementing-mlops?
ancoleman/ai-design-components/implementing-mlops
$npx -y skills add ancoleman/ai-design-components --skill implementing-mlopsInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/ancoleman/ai-design-components" --skill "ancoleman/ai-design-components/implementing-mlops"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/ancoleman/ai-design-components" that are relevant to the current task. Run `npx skills add "https://github.com/ancoleman/ai-design-components"` and select the relevant skills, then follow their instructions.
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