Data Science Implications #
Karma treats infrastructure not as a static plan, but as a dynamic, evolving graph of decisions.
This unlocks rich analysis, pattern discovery, and machine learning — not just for infrastructure optimization, but for understanding how systems evolve over time.
With every deployment, Karma turns infrastructure into structured, queryable data.
Infrastructure as a Dataset #
Each Karma-managed component:
- Has a stable identity (
nickname, project, environment) - Ingests versioned config from Git
- Emits runtime outputs validated by Terraform
- Exists as a node in a live graph stored in Amazon Neptune
- Tracks lineage: what changed, when, by whom — and why
This turns deployments into structured observations.
You can now ask:
- Which changes cause the most downstream breakage?
- What config patterns correlate with stable systems?
- How often does runtime drift from config — and for how long?
- What components tend to fail validation on first deploy?
- How many deployments precede a successful switchover?
Graph Learning and Runtime Signals #
Karma’s Neptune-backed graph enables:
- Learning embeddings for components and relationships
- Detecting anomalies in structure or dependency shifts
- Simulating proposed graph mutations before they’re applied
- Building classifiers that predict:
- Deployment success
- Risk of drift
- Runtime health
- Optimal reconfiguration paths
The infrastructure graph becomes a living, learnable system — capable of introspection and guided improvement.
Event Streams and Feedback Loops #
Karma logs every relevant event:
- Config changes
- Graph updates
- Terraform actions
- Runtime output deltas
- Validation failures
- Change request metadata
This enables reinforcement-like feedback:
You can train models on historical outcomes and improve change routing, deployment policy, or approval logic.
Example: prefer promotion strategies that maximize long-term runtime convergence.
Use Cases #
- Visualizing drift over time as graph deltas
- Forecasting rollback risk based on graph topology
- Recommending safe dependency rewrites
- Modeling config churn vs. incident volume
- Detecting anti-patterns in graph shape or component coupling
- Auto-suggesting required fields or missing config based on similarity
Summary #
Karma transforms infrastructure into a causal graph — not just a snapshot of what exists, but a story of how it got there.
Once infrastructure becomes structured and observable, it becomes learnable.
If you can track it, you can analyze it.
If you can analyze it, you can design smarter systems.