🧠 SmartOps — AI-Driven Predictive Monitoring & Anomaly Detection in DevOps

Illustrative framework for AI-driven predictive monitoring and anomaly detection
in DevOps, BFSI, and cloud observability environments.

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📘 Overview

SmartOps combines classic anomaly detection algorithms (Isolation Forest, One-Class SVM, Autoencoder)
with policy-driven orchestration to predict issues, prevent failures, and trigger auto-remediation.

• Learns operational baselines from telemetry (CPU, latency, APIs, logs)
• Detects deviations indicating potential incidents
• Validates anomalies via guardrails
• Performs safe auto-remediation actions
• Generates human-readable evidence for research and EB1A documentation

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🧩 Architecture Diagram

flowchart LR
  A[Sources: Logs & Metrics] --> B[Collectors]
  B --> C[Feature Engineering]
  C --> D1[Detector: Isolation Forest]
  C --> D2[Detector: One-Class SVM]
  C --> D3[Detector: Autoencoder]
  D1 --> E[Ensemble & Scoring]
  D2 --> E
  D3 --> E
  E --> F[Rules Engine: Policies & Guardrails]
  F --> G{Action?}
  G -- yes --> H[Remediator: Playbooks, Tickets, Runbooks]
  G -- no --> I[Observe & Notify]
  H --> J[Feedback Loop: Outcomes → Model Tuning]
  I --> J

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Predictive Monitoring learns “normal” system behavior using multiple anomaly detectors, fuses results, and triggers automated or guided remediation.

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⚙️ Example Run

# Clone the repository
git clone https://github.com/jbazkar/smartops-framework.git
cd smartops-framework

# Run the illustrative SmartOps pipeline
python examples/simulate_pipeline.py --config configs/sample_config.yaml

This executes a sanitized simulation where SmartOps analyzes synthetic telemetry, applies rules, and performs a dry-run auto-remediation.

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🗂 Repository Structure

smartops-framework/
├─ smartops/                  # Core modules
│  ├─ collectors/             # Data collectors
│  ├─ features/               # Feature engineering
│  ├─ detectors/              # ML models (IForest, OCSVM, Autoencoder)
│  ├─ fusion/                 # Ensemble fusion logic
│  ├─ rules/                  # Guardrails and thresholds
│  ├─ orchestrators/          # Pipeline orchestrators
│  └─ remediation/            # Remediation actions
├─ configs/                   # YAML configurations
├─ examples/                  # Example pipelines
├─ results_banking/           # BFSI synthetic datasets
├─ docs/                      # Architecture & Evidence
├─ assets/                    # Images and supporting visuals
├─ tests/                     # Unit/smoke tests
├─ LICENSE, CITATION.cff, README.md
└─ .github/workflows/         # CI automation

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📊 Evidence Highlights

Section 5 – Predictive Monitoring Results & Evidence

Demonstrates early-stage anomaly detection and quantified improvement after AI SmartOps integration.
📄 docs/evidence.md

• AI Predictive Monitoring Curve
  

• Quantitative Results (Before vs After)
  

Section 6 – Before vs After AI Validation

Shows measurable impact of AI on detection accuracy and false-positive reduction.

Metric	Before	After	Improvement
Anomaly Detection Accuracy	70 %	92 %	+22 %
False Positives	30 %	5 %	−25 %

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🧾 Licensing & Citation

© 2025 Baskaran Jeyarajan (Baskar)
Licensed under the Apache 2.0 License.

For academic references, please cite:

@software{Jeyarajan_SmartOps_2025,
  author       = {Baskaran Jeyarajan},
  title        = {SmartOps: AI-Driven Predictive Monitoring and Anomaly Detection in DevOps},
  year         = {2025},
  publisher    = {GitHub},
  url          = {https://github.com/jbazkar/smartops-framework},
  license      = {Apache-2.0}
}

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🌐 Connect & Contributions

💬 Author: Baskaran Jeyarajan (Baskar)
🤝 Pull requests and issue reports are welcome — please see CONTRIBUTING.md.

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_{© 2025 Baskaran Jeyarajan. All Rights Reserved. | AI SmartOps Framework}