Design a system for managing a distributed machine learning pipeline.

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Anonymous · Accepted Answer

Design a distributed ML pipeline as an event-driven system with clear contracts
between stages: ingestion → validation → feature engineering → training →
evaluation → deployment → monitoring. Raw data lands in a data lake/warehouse
and triggers workflows (Airflow/Argo/Kubeflow) that run containerized jobs on
Kubernetes/Spark/Ray; every dataset and feature is versioned (Delta/Iceberg + a
feature store) and tracked with lineage/metadata. Training is orchestrated on
scalable compute (GPU/CPU pools) with experiment tracking and a model registry
(metrics, artifacts, approvals), and promotion is gated by automated tests (data
quality checks, schema drift, performance, fairness) plus canary/shadow
deployments. Online inference is served via a low-latency service (REST/gRPC)
with autoscaling, while monitoring continuously checks data drift, model
performance, latency, and cost, triggering retraining or rollback; security and
reliability are baked in via IAM, secrets management, encryption, audit logs,
retries, idempotency, and SLOs across each stage.

Design a system for managing a distributed machine learning pipeline.

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