Editing Ai Infrastructure Mlops (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production ML infrastructure system:

'''1. Data infrastructure layer'''
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Raw data sources (databases, streaming, APIs)
    ↓
[Data lake: S3, GCS, ADLS — raw storage]
    ↓
[Data processing: Spark / dbt / Flink — transform to features]
    ↓
[Feature store: online (Redis/DynamoDB) + offline (Parquet/Delta Lake)]
    ↓
[Data versioning: DVC, Delta Lake time travel]
    ↓
[Data quality: Great Expectations, Deequ — validate before training]
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'''2. Training infrastructure'''
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Experiment definition (config file: model, hyperparameters, dataset)
    ↓
[Orchestrator: Kubeflow / Airflow — schedule training job]
    ↓
[Distributed training: GPU cluster with FSDP/DeepSpeed]
    ↓
[Experiment tracking: MLflow / W&B — log metrics, artifacts]
    ↓
[Model evaluation: automated test suite + holdout evaluation]
    ↓
[Model registry: promote to Staging if metrics pass thresholds]
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'''3. Serving infrastructure'''
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Model from registry
    ↓
[Container image build: Docker + model artifact]
    ↓
[Canary deployment: 5% traffic → new model]
    ↓
[A/B test: monitor business KPIs + latency]
    ↓
[Promote to 100% if no regression; rollback if regression detected]
    ↓
[Inference API: FastAPI / Triton / vLLM behind load balancer]
    ↓
[Auto-scaling: scale replicas on GPU utilization / queue depth]
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'''4. Monitoring and retraining loop'''
* Real-time prediction logging with sampling (100% is too expensive)
* Statistical drift tests run daily on sampled prediction distributions
* Alert on: latency SLO breach, drift detected, business KPI degradation
* Automated retraining triggered by drift alerts or scheduled (weekly)
* Human approval gate before promoting retrained model to production

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:MLOps]]
[[Category:AI Infrastructure]]
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