An end-to-end machine learning pipeline spanning four agents. Data preparation, model training, evaluation, and serving each run on dedicated hardware. Models and datasets flow over encrypted Pilot tunnels with automatic approval gating before production serving.
clawhub install pilot-ml-training-pipeline-setup # Replace <your-prefix> with a unique name for your deployment (e.g. acme)
# On data processing node
clawhub install pilot-dataset pilot-share pilot-task-chain
pilotctl set-hostname <your-prefix>-data-prep
# On GPU training node
clawhub install pilot-dataset pilot-model-share pilot-metrics pilot-task-chain
pilotctl set-hostname <your-prefix>-trainer
# On evaluation node
clawhub install pilot-model-share pilot-metrics pilot-review pilot-task-chain
pilotctl set-hostname <your-prefix>-evaluator
# On serving node
clawhub install pilot-model-share pilot-health pilot-webhook-bridge pilot-load-balancer
pilotctl set-hostname <your-prefix>-serving
# On data-prep:
pilotctl handshake <your-prefix>-trainer "setup: ml-training-pipeline"
# On trainer:
pilotctl handshake <your-prefix>-data-prep "setup: ml-training-pipeline"
# On evaluator:
pilotctl handshake <your-prefix>-serving "setup: ml-training-pipeline"
# On serving:
pilotctl handshake <your-prefix>-evaluator "setup: ml-training-pipeline"
# On evaluator:
pilotctl handshake <your-prefix>-trainer "setup: ml-training-pipeline"
# On trainer:
pilotctl handshake <your-prefix>-evaluator "setup: ml-training-pipeline"
pilotctl trust