SIENTIAPDE-1243: Initial commit of the model manager project, adding core files and configurations.

This commit introduces the initial project structure, including:
- .env.example: Example environment configuration.
- .github/workflows/quality-gate.yml: CI workflow for quality checks.
- .gitignore: Specifies intentionally untracked files that Git should ignore.
- Makefile: Automation of tasks like docker builds.
- README.md: Project documentation.
- Source code for model management, activities, utils, worker and workflows.
- Test suite.
- Dockerfile for the simulator.
- sonar-project.properties: SonarQube configuration file.
- values.yaml: Helm chart values for deployment.

model-manager/metrics.py

@@ -0,0 +1,72 @@
+"""
+Laborious Metrics Module
+
+This module defines all Prometheus metrics used by the Sientia DataOps Laborious system
+for monitoring and observability. The metrics provide insights into system performance,
+prediction quality, and operational health.
+
+The metrics are designed to be scraped by Prometheus and can be visualized in
+Grafana or other monitoring dashboards to provide real-time visibility into
+the system's operation.
+
+Key Metric Categories:
+- Application Health: Overall system status and availability
+- Prediction Operations: Count and performance of prediction operations
+- Data Quality: Confidence levels and validation results
+- Export Operations: Database and OPC export performance
+- Response Times: Performance monitoring for various operations
+
+Metric Labels:
+- pod_id: Kubernetes pod identifier for multi-instance deployments
+- model_name: Name of the ML model being used
+- pipeline_name: Name of the prediction pipeline
+- opc_server_id: Identifier for OPC server operations
+"""
+
+from prometheus_client import Gauge, Counter, Histogram
+
+# Application health metric
+APP_UP = Gauge(
+    "app_up",
+    "Indicates if the application is running (1) or shutting down (0)",
+    ["pod_id"],
+)
+
+# Core labels used across multiple metrics
+CORE_LABELS = ["pod_id", "model_name", "pipeline_name"]
+
+# Prediction operation metrics
+PREDICTIONS_WRITTEN_COUNT = Counter(
+    "laborious_predictions_written_count",
+    "Number of predictions written to the database table predictions",
+    CORE_LABELS,
+)
+
+# Prediction quality metrics
+PREDICTION_CONFIDENCE_MONITOR = Gauge(
+    "laborious_prediction_confidence_monitor",
+    "Current confidence of each prediction",
+    CORE_LABELS,
+)
+
+# Performance monitoring metrics
+PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
+    "laborious_prediction_response_time_monitor",
+    "Current response time of each prediction",
+    CORE_LABELS,
+    buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
+)
+
+# OPC export metrics
+PREDICTION_OPC_WRITING_COUNT = Counter(
+    "laborious_prediction_opc_writing_count",
+    "Number of predictions written to the OPC server",
+    [*CORE_LABELS, "opc_server_id"],
+)
+
+PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR = Histogram(
+    "laborious_prediction_opc_writing_response_time_monitor",
+    "Current response time of each prediction written to the OPC server",
+    [*CORE_LABELS, "opc_server_id"],
+    buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
+)