AIOps-ML-Pipeline-Resilience-K8s 🧠💧️🔗

A framework leveraging AIOps techniques on Kubernetes to enhance the resilience and reliability of Machine Learning data pipelines. It focuses on predictive failure detection, intelligent root cause analysis assistance, and adaptive response mechanisms.

🚀 The Challenge

ML data pipelines are critical but often fragile. Failures can disrupt model training, retraining, and inference, leading to stale models and poor predictions. Traditional monitoring often reacts too late.

✨ Our AIOps-Driven Solution

This project aims to:

• Predict Failures: Use ML models trained on pipeline telemetry to anticipate issues before they occur.
• Accelerate Diagnosis: Provide intelligent hints for root cause analysis of pipeline failures.
• Automate Smart Responses: Implement adaptive retries, fallbacks, and self-healing actions.
• Optimize Resource Usage: Dynamically adjust resources for pipeline stages on Kubernetes.
• Improve Overall Data Pipeline Reliability for MLOps.

🔑 Key Features (Planned & In-Progress)

• Telemetry Collection Framework: Gathers metrics and logs from pipeline orchestrators (Argo Workflows, Kubeflow Pipelines, Airflow on K8s) and data stages.
• AIOps Engine:
  • Anomaly Detection in pipeline metrics.
  • Predictive models for failure forecasting (e.g., using time series analysis on run durations, error rates).
  • Log pattern analysis for RCA.
• Intelligent Response Controller:
  • Configurable rules for adaptive retries (e.g., increase resources on retry).
  • Automated switching to fallback data sources or cached data.
  • (Future) Simple data self-healing actions.
• Integration with Kubernetes: Leverages K8s for running AIOps components and managing pipeline resources.
• Orchestrator Adapters: Pluggable components to interface with different pipeline tools.
• Dashboards & Alerting: Visualization of pipeline health and AIOps insights.

🛠️ Technology Stack (Tentative)

• Python 3.x
• Kubernetes
• Pipeline Orchestrators: Argo Workflows, Kubeflow Pipelines (initially one)
• Monitoring: Prometheus, Grafana
• AIOps/ML Libraries: scikit-learn, statsmodels, prophet (for forecasting), tensorflow/pytorch (for more complex models), NLP libraries for log analysis.
• (Optional) Message Queue: Kafka/RabbitMQ for event-driven AIOps.
• (Optional) LLM API for advanced log summarization/RCA.

🏁 Getting Started

(This section will be filled in as you build)

1. Clone repository...
2. Deploy monitoring stack (Prometheus/Grafana) on K8s...
3. Deploy AIOps components...
4. Configure adapter for your pipeline orchestrator...
5. See example ML data pipelines with AIOps resilience enabled...

📂 Project Structure (Tentative)

(Describe planned folder structure)

🤝 Contributing

Contributions are highly encouraged! Please see CONTRIBUTING.md. We need help with:

• Developing new AIOps models and algorithms.
• Building adapters for more pipeline orchestrators.
• Creating example resilient data pipelines.
• Improving documentation and dashboards.

📜 License

This project is licensed under the Apache 2.0 License.