How mlops-devops-04-26/aiops-realworld-labs works
1. Repository purpose
- This repo is a small AIOps lab focused on collecting infrastructure/application metrics, training an anomaly-detection model, and running detection against live metrics.
- The architecture is centered around a simple offline/online pipeline rather than a large application service.
2. Top-level structure
- DAY-01/aiops-lab/ contains the actual project.
- The main subsystems are:
- Metric source/configuration in config/
- Operational orchestration in docker-compose.yml
- Python data/ML pipeline in scripts/
3. Monitoring/metrics boundary
- config/prometheus.yml indicates Prometheus is used as the metrics collection layer.
- This is the boundary where raw runtime/service metrics are scraped and made available to the Python pipeline.
- Prometheus is the canonical data source for downstream anomaly detection.
4. Pipeline entry points
- scripts/fetch_metrics.py is the ingestion step: it likely queries Prometheus and pulls metric samples into a local processing flow.
- scripts/train_model.py is the model-building step: it consumes historical metrics and produces a trained anomaly model or model parameters.
- scripts/detect_anomalies.py is the inference step: it applies the trained model to current or recent metrics and flags anomalies.
5. Data flow
- The high-level flow is:
1. Metrics are scraped into Prometheus.
2. fetch_metrics.py retrieves metric data from Prometheus.
3. train_model.py uses historical data to fit a model.
4. detect_anomalies.py scores incoming/recent metrics against that model.
- This creates a clear split between data acquisition, training, and real-time detection.
6. Runtime and dependencies
- The Python code lives under scripts/, and scripts/requirements.txt defines the ML/data-processing runtime dependencies.
- This suggests the repo is designed to be run in a controlled Python environment, likely alongside the metrics stack defined in Docker.
7. Container/orchestration layer
- docker-compose.yml is the environment orchestration boundary.
- It likely brings up the monitoring stack and any supporting services needed to run the lab consistently.
- This makes the repo suitable for reproducible experiments rather than ad hoc local execution.
8. Configuration separation
- scripts/config.ini suggests runtime parameters are externalized from code.
- This likely holds connection details, thresholds, time windows, or model settings, keeping the scripts environment-agnostic.
- The repo therefore has a clean split between code, runtime configuration, and infrastructure config.
9. Responsibility boundaries
- Prometheus/config: metric collection and exposure
- Fetch script: data retrieval from monitoring backend
- Training script: offline learning from historical observations
- Detection script: inference and anomaly flagging
- Compose file: environment lifecycle and service wiring
10. Likely architecture style
- This is a batch + near-real-time analytics pipeline rather than a request/response application.
- The architecture is centered on metrics telemetry and model-driven detection, with Prometheus as the system-of-record for observability data.
11. What an architecture diagram should show
- A metrics-producing system feeding Prometheus.
- Prometheus as the central store/query endpoint.
- Separate Python jobs for:
- metric extraction
- model training
- anomaly detection
- Shared configuration feeding the Python jobs.
- Docker Compose as the deployment/orchestration wrapper.
12. Project scope
- The repository appears to be a single-lab, self-contained demo for AIOps workflows.
- There is no sign of a multi-service product backend; instead, the repo emphasizes observability data handling and ML-based operational analysis.
Main entry points: fetch_metrics.py Fetch Metrics · train_model.py Train Model · detect_anomalies.py Detect Anomalies · docker-compose.yml Compose
Components 9
Orchestration
Environment wiring and reproducible lab setup
- Compose · Container and service lifecycle for the lab
DAY-01/aiops-lab/docker-compose.yml
Monitoring
Metrics collection and query boundary
- Metrics Source · Infrastructure or application metrics emitted by the lab environment
- Prometheus Config · Scrape targets and collection rules for metrics ingestion
DAY-01/aiops-lab/config/prometheus.yml - Prometheus · Central scrape and query layer for telemetry
Python Pipeline
Data acquisition, model training, and anomaly detection
- Runtime Config · Shared parameters for Python jobs such as endpoints, windows, or thresholds
DAY-01/aiops-lab/scripts/config.ini - Python Deps · Python data and machine-learning environment dependencies
DAY-01/aiops-lab/scripts/requirements.txt - Fetch Metrics · Pulls metric samples from Prometheus into local processing
DAY-01/aiops-lab/scripts/fetch_metrics.py - Train Model · Fits anomaly-detection logic from historical metrics
DAY-01/aiops-lab/scripts/train_model.py - Detect Anomalies · Scores recent metrics and flags anomalies
DAY-01/aiops-lab/scripts/detect_anomalies.py
Connections 16
- Metrics Source → Prometheus Config: scraped by · Metrics are exposed through the scrape configuration
- Prometheus Config → Prometheus: feeds · Scrape rules define what Prometheus collects
- Compose → Prometheus: starts · Compose brings up the monitoring backend in the lab environment
- Compose → Fetch Metrics: runs · Compose provides the execution environment for the Python jobs
- Compose → Train Model: runs · Compose provides the execution environment for the Python jobs
- Compose → Detect Anomalies: runs · Compose provides the execution environment for the Python jobs
- Prometheus → Fetch Metrics: queries · The ingestion step pulls samples from Prometheus
- Runtime Config → Fetch Metrics: configures · Shared settings drive Prometheus access and extraction windows
- Runtime Config → Train Model: configures · Shared settings drive training inputs and parameters
- Runtime Config → Detect Anomalies: configures · Shared settings drive detection thresholds and inputs
- Python Deps → Fetch Metrics: supports · Runtime dependencies enable data access and processing
- Python Deps → Train Model: supports · Runtime dependencies enable model fitting
- Python Deps → Detect Anomalies: supports · Runtime dependencies enable scoring logic
- Fetch Metrics → Train Model: historical data · Retrieved metrics feed the offline training step
- Fetch Metrics → Detect Anomalies: recent data · Retrieved metrics also support live or near-real-time detection
- Train Model → Detect Anomalies: model · Trained parameters are consumed by the inference step