Deploy Ml Model Serving
技能 活跃Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.
To enable users to deploy and manage machine learning models in production environments with robust, scalable, and observable serving infrastructure.
功能
- Deploy ML models with MLflow, BentoML, Seldon Core
- Implement REST/gRPC endpoints for real-time inference
- Configure autoscaling for variable load
- Set up monitoring and observability with Prometheus/Grafana
- Implement A/B testing and canary deployments
使用场景
- Deploying trained models for real-time inference
- Setting up prediction APIs
- Implementing autoscaling for fluctuating demand
- Running A/B tests between model versions
- Migrating from batch to real-time inference
非目标
- Training or fine-tuning ML models
- Managing MLflow tracking server or Kubernetes cluster infrastructure
- Directly interacting with cloud provider deployment services (e.g., SageMaker, Vertex AI)
- Performing deep model performance analysis or drift detection (beyond basic monitoring)
Documentation
- info:Configuration & parameter referenceWhile the SKILL.md provides good procedural steps and examples, it does not explicitly document all configuration parameters, defaults, or precedence orders for external tools like MLflow, BentoML, or Kubernetes.
Portability
- warning:Structural AssumptionThe example Kubernetes YAMLs and Dockerfiles assume a certain project structure and environment setup (e.g., 'your-registry/churn-classifier:v1.0', 'http://mlflow-server:5000') that might require significant adaptation by the user.
- warning:Runtime stabilityThe skill relies heavily on specific tooling (MLflow, BentoML, Seldon Core, Kubernetes) and assumes their presence and proper configuration, which may lead to instability if the user's environment differs significantly.
Errors
- info:Actionable error messagesWhile the SKILL.md outlines potential failures and recovery steps for specific deployment stages, it does not provide universally actionable error messages for all potential runtime issues within the deployed infrastructure.
Execution
- warning:Pinned dependenciesThe SKILL.md mentions dependencies for MLflow, BentoML, and Seldon Core, and Dockerfiles include `pip install` commands, but specific versions are not always pinned, and lockfiles for Python dependencies are not explicitly referenced, which could lead to dependency conflicts.
Practical Utility
- warning:Edge casesThe SKILL.md lists common pitfalls and provides some recovery steps, but it does not systematically document all potential failure modes (e.g., dependency conflicts, network issues, specific K8s errors) with clear symptoms and recovery paths.
Safety
- info:Halt on unexpected stateThe skill outlines potential failure points and recovery steps, but it does not explicitly mandate halting the workflow or reporting on unexpected pre-state in a machine-readable checklist format.
安装
/plugin install agent-almanac@pjt222-agent-almanac质量评分
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