Lilly Tech Systems
Designing AI Data Pipelines
Build production-grade data infrastructure that feeds ML systems reliably at scale. Learn to design batch and streaming pipelines for feature computation, enforce data quality gates that prevent bad data from reaching models, version datasets for reproducible training, and monitor pipeline health with SLAs and alerting — the complete engineering playbook for data and ML engineers.
Your Learning Path
Follow these lessons in order for a complete understanding of AI data pipeline design, or jump to any topic that interests you.
1. Data Pipeline Architecture for ML
How ML data pipelines differ from analytics, ETL vs ELT for ML, pipeline orchestration patterns, data contracts, and why your model is only as good as your data pipeline.
2. Batch Data Pipelines
PySpark and Dask for feature computation, partitioning strategies, idempotent processing, backfill patterns, and production-ready batch pipeline code.
3. Streaming Data Pipelines
Kafka + Flink/Spark Streaming for real-time features, exactly-once semantics, windowed aggregations, stream-batch unification, and production code examples.
4. Data Quality & Validation
Schema validation, distribution drift detection, missing data handling, Great Expectations and Pandera integration, and automated data quality gates.
5. Data Versioning & Lineage
DVC, Delta Lake time travel, dataset versioning strategies, lineage tracking with OpenLineage, and reproducible datasets for training.
6. Pipeline Monitoring & Observability
Data freshness monitoring, pipeline SLAs, alerting on data quality issues, cost tracking, and building Datadog/Grafana dashboards for pipeline health.
7. Best Practices & Checklist
Production checklist for data pipelines, common pipeline failures, debugging data issues, and frequently asked questions about ML data infrastructure.
What You'll Learn
By the end of this course, you will be able to:
Design Data Pipelines for ML
Architect batch and streaming data infrastructure that delivers reliable, fresh features to ML models at scale — with proper orchestration, contracts, and SLAs.
Enforce Data Quality Gates
Build automated validation pipelines that catch schema violations, distribution drift, and data anomalies before they corrupt your models.
Version and Track Datasets
Implement dataset versioning, lineage tracking, and time travel so every training run is reproducible and every data transformation is auditable.
Monitor Pipeline Health
Build observability dashboards with freshness SLAs, quality metrics, cost tracking, and automated alerting that catch pipeline failures before your models degrade.