feat(devops): CI/CD + Docker + Monitoring infrastructure

- Multi-stage Dockerfile (builder+runner, <500MB target)
- docker-compose.yml: app(x4) + postgres + redis + prometheus + grafana + nginx
- .env.example with all required secrets (never hardcoded)
- requirements.txt with all dependencies including prometheus-client, alembic
- GitHub Actions CI: lint (flake8+bandit+safety) + tests + Docker build/push
- GitHub Actions CD: staging deploy -> smoke tests -> production deploy + rollback
- Alembic migration setup + initial PostgreSQL schema (001_initial_schema)
- SQLite→PostgreSQL data migration script
- Prometheus metrics module (HTTP, ML, DB, business metrics)
- Prometheus alert rules (5xx >1%, latency >2s, disk >80%, ML accuracy)
- Grafana dashboard (overview: req/s, p95, ML accuracy, error rate)
- Nginx reverse proxy config (HTTPS/TLS, rate limiting, security headers)
- Structured JSON logging module
- Automated daily DB backup script (pg_dump + 30-day retention)

Branch: feature/devops-cicd

Co-Authored-By: Paperclip <noreply@paperclip.ing>

infra/grafana/dashboards/turf-saas-overview.json

@@ -0,0 +1,174 @@
+{
+  "title": "Turf SaaS — Overview",
+  "uid": "turf-saas-overview",
+  "schemaVersion": 38,
+  "version": 1,
+  "refresh": "30s",
+  "time": { "from": "now-6h", "to": "now" },
+  "tags": ["turf-saas"],
+  "panels": [
+    {
+      "id": 1,
+      "type": "stat",
+      "title": "Request Rate (req/s)",
+      "gridPos": { "h": 4, "w": 6, "x": 0, "y": 0 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "sum(rate(http_requests_total[5m]))",
+          "legendFormat": "req/s"
+        }
+      ],
+      "options": { "colorMode": "background", "graphMode": "area" }
+    },
+    {
+      "id": 2,
+      "type": "stat",
+      "title": "Error Rate (5xx)",
+      "gridPos": { "h": 4, "w": 6, "x": 6, "y": 0 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "sum(rate(http_requests_total{status_code=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m])) * 100",
+          "legendFormat": "error %"
+        }
+      ],
+      "fieldConfig": {
+        "defaults": {
+          "unit": "percent",
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              { "color": "green", "value": null },
+              { "color": "yellow", "value": 0.5 },
+              { "color": "red", "value": 1 }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "id": 3,
+      "type": "stat",
+      "title": "p95 Latency",
+      "gridPos": { "h": 4, "w": 6, "x": 12, "y": 0 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))",
+          "legendFormat": "p95"
+        }
+      ],
+      "fieldConfig": {
+        "defaults": {
+          "unit": "s",
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              { "color": "green", "value": null },
+              { "color": "yellow", "value": 1 },
+              { "color": "red", "value": 2 }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "id": 4,
+      "type": "stat",
+      "title": "ML Top-1 Accuracy",
+      "gridPos": { "h": 4, "w": 6, "x": 18, "y": 0 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "ml_prediction_accuracy_ratio{accuracy_type=\"top1\"} * 100",
+          "legendFormat": "top-1 %"
+        }
+      ],
+      "fieldConfig": {
+        "defaults": {
+          "unit": "percent",
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              { "color": "red", "value": null },
+              { "color": "yellow", "value": 25 },
+              { "color": "green", "value": 35 }
+            ]
+          }
+        }
+      }
+    },
+    {
+      "id": 5,
+      "type": "timeseries",
+      "title": "HTTP Requests by Service",
+      "gridPos": { "h": 8, "w": 12, "x": 0, "y": 4 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "sum(rate(http_requests_total[5m])) by (service)",
+          "legendFormat": "{{ service }}"
+        }
+      ],
+      "fieldConfig": {
+        "defaults": { "unit": "reqps" }
+      }
+    },
+    {
+      "id": 6,
+      "type": "timeseries",
+      "title": "Request Duration p50/p95/p99",
+      "gridPos": { "h": 8, "w": 12, "x": 12, "y": 4 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "histogram_quantile(0.50, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))",
+          "legendFormat": "p50"
+        },
+        {
+          "datasource": "Prometheus",
+          "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))",
+          "legendFormat": "p95"
+        },
+        {
+          "datasource": "Prometheus",
+          "expr": "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))",
+          "legendFormat": "p99"
+        }
+      ],
+      "fieldConfig": {
+        "defaults": { "unit": "s" }
+      }
+    },
+    {
+      "id": 7,
+      "type": "timeseries",
+      "title": "ML Predictions per Hour",
+      "gridPos": { "h": 8, "w": 12, "x": 0, "y": 12 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "sum(increase(ml_predictions_total[1h])) by (model_type)",
+          "legendFormat": "{{ model_type }}"
+        }
+      ]
+    },
+    {
+      "id": 8,
+      "type": "timeseries",
+      "title": "DB Query Duration",
+      "gridPos": { "h": 8, "w": 12, "x": 12, "y": 12 },
+      "targets": [
+        {
+          "datasource": "Prometheus",
+          "expr": "histogram_quantile(0.95, sum(rate(db_query_duration_seconds_bucket[5m])) by (le, operation))",
+          "legendFormat": "{{ operation }} p95"
+        }
+      ],
+      "fieldConfig": {
+        "defaults": { "unit": "s" }
+      }
+    }
+  ]
+}