This workflow follows the Executecommand → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"name": "MLOps Pipeline - Hand Talk",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "mlops-pipeline",
"responseMode": "responseNode",
"options": {}
},
"id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
240,
300
]
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "p1",
"name": "dataset_name",
"value": "={{ (() => { const value = $json.body?.dataset_name; return value === undefined || value === null || value === '' || value === 'undefined' ? 'para_crawl/enpt' : String(value); })() }}",
"type": "string"
},
{
"id": "p2",
"name": "max_tokens",
"value": "={{ (() => { const value = $json.body?.max_tokens; return value === undefined || value === null || value === '' || value === 'undefined' ? 64 : Number(value); })() }}",
"type": "number"
},
{
"id": "p3",
"name": "epochs",
"value": "={{ (() => { const value = $json.body?.epochs; return value === undefined || value === null || value === '' || value === 'undefined' ? 2 : Number(value); })() }}",
"type": "number"
},
{
"id": "p4",
"name": "threshold",
"value": "={{ (() => { const value = $json.body?.threshold; return value === undefined || value === null || value === '' || value === 'undefined' ? 0.0 : Number(value); })() }}",
"type": "number"
},
{
"id": "p5",
"name": "git_sha",
"value": "={{ (() => { const value = $json.body?.git_sha; return value === undefined || value === null || value === '' || value === 'undefined' ? 'unknown' : String(value); })() }}",
"type": "string"
}
]
},
"options": {}
},
"id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
"name": "Par\u00e2metros",
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
460,
300
]
},
{
"parameters": {
"command": "=docker run --rm --network mlops-challenge_mlops -v {{ $env.HOST_WORKSPACE_PATH }}:/workspace -e WRAPT_DISABLE_EXTENSIONS=1 -e PYTHONUNBUFFERED=1 mlops-challenge-ml:local python -m ml.prepare_dataset --output_dir data/processed --dataset_name {{ $json.dataset_name }} --max_tokens {{ $json.max_tokens }} --train_records 500 --val_records 100 --seed 42"
},
"id": "c3d4e5f6-a7b8-9012-cdef-123456789012",
"name": "Preparar Dados",
"type": "n8n-nodes-base.executeCommand",
"typeVersion": 1,
"position": [
680,
300
]
},
{
"parameters": {
"command": "=docker run --rm --network mlops-challenge_mlops -v {{ $env.HOST_WORKSPACE_PATH }}:/workspace -e WRAPT_DISABLE_EXTENSIONS=1 -e PYTHONUNBUFFERED=1 mlops-challenge-ml:local python -m ml.train --data_dir data/processed --artifacts_dir artifacts --epochs {{ $('Par\u00e2metros').item.json.epochs }} --batch_size 32 --threshold {{ $('Par\u00e2metros').item.json.threshold }} --max_tokens {{ $('Par\u00e2metros').item.json.max_tokens }} --git_sha {{ $('Par\u00e2metros').item.json.git_sha }}"
},
"id": "d4e5f6a7-b8c9-0123-defa-234567890123",
"name": "Treinar",
"type": "n8n-nodes-base.executeCommand",
"typeVersion": 1,
"position": [
900,
300
]
},
{
"parameters": {
"jsCode": "const output = $input.item.json.stdout;\nconst lines = output.trim().split('\\n');\nconst lastLine = lines[lines.length - 1];\nconst summary = JSON.parse(lastLine);\nreturn { json: summary };"
},
"id": "e5f6a7b8-c9d0-1234-efab-345678901234",
"name": "Parsear Resultado",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1120,
300
]
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"typeValidation": "strict"
},
"conditions": [
{
"id": "check-approved",
"leftValue": "={{ $json.status }}",
"rightValue": "approved",
"operator": {
"type": "string",
"operation": "equals"
}
}
],
"combinator": "and"
},
"options": {}
},
"id": "f6a7b8c9-d0e1-2345-fabc-456789012345",
"name": "Aprovado?",
"type": "n8n-nodes-base.if",
"typeVersion": 2,
"position": [
1340,
300
]
},
{
"parameters": {
"method": "GET",
"url": "http://api:8000/model",
"options": {}
},
"id": "a0b1c2d3-e4f5-6789-abcd-000000000001",
"name": "Modelo Atual",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1560,
180
]
},
{
"parameters": {
"command": "=docker run --rm --network mlops-challenge_mlops -v {{ $env.HOST_WORKSPACE_PATH }}:/workspace -e WRAPT_DISABLE_EXTENSIONS=1 -e PYTHONUNBUFFERED=1 mlops-challenge-ml:local python automation/publish_artifact.py --artifacts_dir artifacts --run_id {{ $('Parsear Resultado').item.json.run_id }}"
},
"id": "11223344-5566-7788-99aa-bbccddeeff00",
"name": "Publicar Artefatos",
"type": "n8n-nodes-base.executeCommand",
"typeVersion": 1,
"position": [
1560,
300
]
},
{
"parameters": {
"method": "POST",
"url": "http://api:8000/reload",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ JSON.stringify({ run_id: $('Parsear Resultado').item.json.run_id, artifacts_dir: 'artifacts' }) }}",
"options": {}
},
"onError": "continueErrorOutput",
"id": "a7b8c9d0-e1f2-3456-abcd-567890123456",
"name": "Deploy",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1780,
180
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify({ success: true, run_id: $('Parsear Resultado').item.json.run_id, status: 'deployed', metric_value: $('Parsear Resultado').item.json.metric_value }) }}",
"options": {}
},
"id": "b8c9d0e1-f2a3-4567-bcde-678901234567",
"name": "Sucesso",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
2000,
120
]
},
{
"parameters": {
"method": "POST",
"url": "http://api:8000/reload",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ JSON.stringify({ run_id: $('Modelo Atual').item.json.run_id || null, artifacts_dir: 'artifacts' }) }}",
"options": {}
},
"id": "a0b1c2d3-e4f5-6789-abcd-000000000002",
"name": "Rollback",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
2000,
300
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify({ success: false, run_id: $('Parsear Resultado').item.json.run_id, status: 'deploy_failed_rollback', reason: 'Falha no deploy \u2014 modelo anterior restaurado', previous_run_id: $('Modelo Atual').item.json.run_id }) }}",
"options": {
"responseCode": 500
}
},
"id": "a0b1c2d3-e4f5-6789-abcd-000000000003",
"name": "Falha no Deploy",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
2220,
300
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify({ success: false, run_id: $('Parsear Resultado').item.json.run_id, status: 'rejected', reason: 'Modelo abaixo do threshold', metric_value: $('Parsear Resultado').item.json.metric_value, threshold: $('Parsear Resultado').item.json.threshold }) }}",
"options": {
"responseCode": 422
}
},
"id": "c9d0e1f2-a3b4-5678-cdef-789012345678",
"name": "Rejeitado",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
1560,
420
]
},
{
"parameters": {
"httpMethod": "POST",
"path": "mlops-alerts",
"responseMode": "responseNode",
"options": {}
},
"id": "00112233-4455-6677-8899-aabbccddeeff",
"name": "Webhook Alertas",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
240,
560
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify({ received: true, source: 'grafana', status: 'acknowledged', alert: $json.body?.title || $json.body?.message || 'unknown' }) }}",
"options": {}
},
"id": "ffeeddcc-bbaa-9988-7766-554433221100",
"name": "Alerta Recebido",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
460,
560
]
}
],
"connections": {
"Webhook": {
"main": [
[
{
"node": "Par\u00e2metros",
"type": "main",
"index": 0
}
]
]
},
"Par\u00e2metros": {
"main": [
[
{
"node": "Preparar Dados",
"type": "main",
"index": 0
}
]
]
},
"Preparar Dados": {
"main": [
[
{
"node": "Treinar",
"type": "main",
"index": 0
}
]
]
},
"Treinar": {
"main": [
[
{
"node": "Parsear Resultado",
"type": "main",
"index": 0
}
]
]
},
"Parsear Resultado": {
"main": [
[
{
"node": "Aprovado?",
"type": "main",
"index": 0
}
]
]
},
"Aprovado?": {
"main": [
[
{
"node": "Publicar Artefatos",
"type": "main",
"index": 0
}
],
[
{
"node": "Rejeitado",
"type": "main",
"index": 0
}
]
]
},
"Publicar Artefatos": {
"main": [
[
{
"node": "Modelo Atual",
"type": "main",
"index": 0
}
]
]
},
"Modelo Atual": {
"main": [
[
{
"node": "Deploy",
"type": "main",
"index": 0
}
]
]
},
"Deploy": {
"main": [
[
{
"node": "Sucesso",
"type": "main",
"index": 0
}
]
],
"error": [
[
{
"node": "Rollback",
"type": "main",
"index": 0
}
]
]
},
"Rollback": {
"main": [
[
{
"node": "Falha no Deploy",
"type": "main",
"index": 0
}
]
]
},
"Webhook Alertas": {
"main": [
[
{
"node": "Alerta Recebido",
"type": "main",
"index": 0
}
]
]
}
},
"active": true,
"settings": {
"executionOrder": "v1"
},
"versionId": "d0e1f2a3-b4c5-6789-defa-890123456790",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "f2a3b4c5-d6e7-8901-fabc-012345678901",
"tags": []
}
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About this workflow
MLOps Pipeline - Hand Talk. Uses executeCommand, httpRequest. Webhook trigger; 15 nodes.
Source: https://github.com/saviorgomes/mlops-challenge-handtalk/blob/52c3dfbaaa663a25f3840a84379d412355049005/n8n/workflow.json — original creator credit. Request a take-down →
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