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From godmode
Manages MLOps workflows: deploys models, handles versioning/A/B testing/drift detection/retraining/monitoring. Generates YAML configs and git commits. Supports flags like --deploy, --status, --drift.
npx claudepluginhub arbazkhan971/godmodeHow this command is triggered — by the user, by Claude, or both
Slash command
/godmode:mlopsgodmode/The summary Claude sees in its command listing — used to decide when to auto-load this command
# /godmode:mlops MLOps and model deployment management. Handles model serving infrastructure, inference optimization, model versioning, A/B testing, drift detection, and automated retraining. Supports TensorFlow Serving, Triton Inference Server, SageMaker, and custom serving solutions. ## Usage ## What It Does 1. Assesses model readiness (evaluation, bias, latency, size, error handling, compliance) 2. Configures serving infrastructure (TensorFlow Serving, Triton, SageMaker, FastAPI/Ray Serve) 3. Optimizes inference (FP16/INT8 quantization, ONNX, TensorRT, pruning, distillation, batchi...
/mlopsGenerates MLOps strategy document covering model lifecycle, training pipelines, serving, monitoring, and governance.
/build-ml-pipelineBuilds end-to-end ML pipeline from data ingestion, preprocessing, training, evaluation, to production deployment with MLOps, given problem description.
/shipAssesses deployment readiness of a trained model, generates standardized model card with 8 sections, and produces readiness decision (Ready/Conditionally/Not Ready).
/fal-deployDeploys custom ML models to fal.ai serverless infrastructure with GPU support. Generates fal.App Python code including setup, endpoints, scaling, dependencies, and deployment commands.
/deploy-modelAnalyzes context to execute AI/ML tasks: generates code with validation and error handling, provides metrics and insights, saves artifacts, and creates documentation.
Share bugs, ideas, or general feedback.
MLOps and model deployment management. Handles model serving infrastructure, inference optimization, model versioning, A/B testing, drift detection, and automated retraining. Supports TensorFlow Serving, Triton Inference Server, SageMaker, and custom serving solutions.
/godmode:mlops # Interactive model deployment workflow
/godmode:mlops --status # Production monitoring dashboard
/godmode:mlops --deploy <model> # Deploy a specific model version
/godmode:mlops --promote # Promote canary to champion
/godmode:mlops --rollback # Rollback to previous champion
/godmode:mlops --drift # Run drift detection analysis
/godmode:mlops --retrain # Trigger retraining pipeline
/godmode:mlops --ab-test # Configure or check A/B test
/godmode:mlops --optimize # Run inference optimization benchmarks
/godmode:mlops --scale <replicas> # Scale serving infrastructure
/godmode:mlops --versions # Show model version registry
configs/mlops/<model>-serving.yamlconfigs/mlops/<model>-monitoring.yaml"mlops: <model> v<version> — <action> (<platform>)"Train (ml) → Readiness Check → Deploy Canary (5%) → A/B Test → Promote Champion
↓
Monitor → Drift Detection → Retrain
If deployed: /godmode:mlops --status to monitor health.
If drift detected: /godmode:ml to review retraining results.
If A/B test done: /godmode:mlops --promote or --rollback.
/godmode:mlops --deploy ticket-classifier-v3.2 # Deploy a model
/godmode:mlops --status # Check production health
/godmode:mlops --drift # Run drift analysis
/godmode:mlops --rollback # Emergency rollback