- Identify: Mannequin-runnerz
- Electronic mail: modelrunnerz@gmail.com
- LinkedIn: linkedin.com/in/model-runnerz-b09102374
In right this moment’s high-velocity DevOps environments, website reliability is challenged by alert fatigue, fragmented observability, and delayed incident decision. This weblog showcases U-InOps, a 100% open-source platform engineered to unify AI/ML, AIOps, LLM, NLP, DevOps, and Platform Engineering into one good, scalable system.
As an skilled ML and DevOps skilled, I constructed this as an enterprise-grade demonstration of full lifecycle venture maturity — from necessities gathering to manufacturing handoff. This put up acts each as a technical deep-dive and a portfolio-ready information switch (KT) asset for interviews.
Think about a hospital the place servers run ICU monitoring and appointment reserving methods. If one fails, lives could also be at stake.
Issues:
- No proactive alerting
- Too many false positives
- Night time-shift engineers delayed in RCA.
U-InOps Answer:
- Detect irregular server habits early
- Auto-log incidents in Jira + notify Slack
- Use LLM to summarize logs into readable incident stories
- Counsel fixes based mostly on historic points.
- Predict and resolve incidents mechanically utilizing ML + AIOps
- Summarize logs and alerts with LLMs (LangChain + Open Supply Fashions)
- Present stay telemetry and incident observability
- Allow DevOps automation utilizing GitOps + Terraform + ArgoCD
- Ship full traceability by way of MLOps (MLflow + DVC)
Logs/Metrics/Traces → Kafka → Flink → MinIO
↓
ML Mannequin (Anomaly Detection)
↓
→ Slack + Jira + Net Dashboard (React)
→ RCA Generator (LangChain + ChromaDB)
Key Instruments:
LayerOpen-Supply ToolsData StreamKafka, Flink, Fluent BitStorageMinIO, PostgreSQLML/MLOpsPyTorch, MLflow, DVC, EvidentlyCI/CDGitHub Actions, ArgoCD, TerraformNLP/LLMLangChain, Hugging Face TransformersObservabilityPrometheus, Grafana, OpenTelemetry, LokiUI & ChatOpsReact + Tailwind, Slackbot (FastAPI)
- ✅ Necessities finalization
- ✅ Confluence docs, Slack workspace, GitHub repo setup
- ✅ Terraform + K8s base infra setup
- ✅ Observability bootstrap with Prometheus & Loki
- ✅ Kafka pipeline for logs and metrics
- ✅ Flink jobs for transformation
- ✅ ML mannequin for anomaly detection (tracked in MLflow + DVC)
- ✅ LangChain integration with ChromaDB
- ✅ Slackbot PoC to summarize incidents
- ✅ React dashboard for metrics/alerts
- ✅ GitOps by way of ArgoCD, check protection by way of pytest & Playwright
- ✅ Mannequin rollback, chaos testing, CEO/PM documentation
- 70%+ accuracy in anomaly detection
- MTTR decreased by ~50%
- 1-click mannequin rollback working in staging
- Slackbot generates summaries for each essential incident
- Showcase your information with traceability: hyperlink Jira ↔️ GitHub ↔️ Confluence
- Clarify trade-offs: why LangChain? why DVC over plain Git?
- All the time doc assumptions, structure, and outcomes
- Follow articulating “What downside does this remedy?”
- GitHub:
uinops-platform-core,uinops-ml-models,uinops-dashboard - Docs: Dash pages, structure diagrams, runbooks (Confluence)
- Stack: 100% open-source, cloud-agnostic, K8s-native
U-InOps is greater than a venture — it’s a template for full-stack ML+AIOps supply. Use it to exhibit enterprise readiness, staff management, and technical depth in your interviews.
Let me know should you’d like the total supply, Confluence templates, or to stroll by way of the stay demo in motion!
- Identify: Mannequin-runnerz
- Electronic mail: modelrunnerz@gmail.com
- LinkedIn: linkedin.com/in/model-runnerz-b09102374
