Automating the ML data supply chain with serverless AWS services
Introduced event-driven extraction, validation, and training orchestration so data scientists reclaimed time from manual CSV exports and models retrain reliably.
Manual analyst effort
26 hrs/month
Training failure rate
95%
Feature freshness
Weekly Intraday
Overview
Data scientists manually exported CSV files from PostgreSQL, validated them with bespoke notebooks, and kicked off ad hoc SageMaker jobs. The fragile chain caused one in five training runs to fail and consumed nearly a full day of analyst time every week.
We co-designed a serverless supply chain that reacts to data changes in near real time, standardizes feature engineering, and enforces validation gates before triggering model retraining. The solution combined automation, observability, and change management tailored to lean ML teams.
Challenges
- Manual extracts and notebook-based cleanup delayed feature availability and introduced human error.
- Model retraining failed frequently because stale partitions or schema changes were detected too late.
- The ML engineering team lacked a repeatable pattern for onboarding new sources without sacrificing governance.
Approach
Event-driven ingestion pipeline
Implemented EventBridge rules and database change signals that trigger Glue jobs to capture incremental updates and land both raw and feature-ready datasets in S3.
Embedded data quality guardrails
Standardized validation Lambdas that enforce schema contracts, row-count thresholds, and distribution checks while publishing telemetry to CloudWatch and Slack.
Managed training orchestration
Coordinated Glue, Lambda, and SageMaker steps within Step Functions so retraining only proceeds after data passes validation, with execution context preserved for lineage.
Impact delivered
- Reduced manual analyst toil by approximately 26 hours each month, shifting focus to feature innovation and experiment design.
- Cut model training failure rates by 95% thanks to automated validation gates and deterministic retraining workflows.
- Delivered near real-time feature availability, moving from weekly refreshes to intraday cadences that improve prediction accuracy.
- Provided reusable infrastructure-as-code blueprints that accelerate onboarding of future ML initiatives without additional headcount.
Key lessons
- Event-driven automation keeps feature pipelines synchronized with transactional systems without manual scheduling.
- Embedding validation and observability into each stage minimizes retraining toil and speeds incident response.
- Reusable infrastructure patterns let small ML teams scale experimentation without sacrificing governance.
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