Distributed scheduler and orchestration layer for AI batch jobs, profiling pipelines, transaction publishing, and model execution workflows.

Batch Processing, MLOps, Profiling

Duration

1 Year

Year

2024

Region

USA

This project is a modular Java batch ecosystem built around ai-scheduler-starter, coordinating profiling set generation, historical statistics, transaction publishing, and beta model execution.

It orchestrates multi-service dependencies and shared utilities so large-scale offline AI operations can run reliably in sequence or parallel, supporting enterprise-grade data/ML workloads.

Problems

AI batch pipelines were fragmented and hard to coordinate at scale.

Opportunities

Standardize scheduling, retries, and orchestration for reusable AI batch services.

Solutions

Introduced parent-managed modules (profiling, evaluation expression, operation execution, publishing, scheduler services) under a common starter and dependency framework.

Future Trends

Event-driven orchestration, SLA-aware scheduling, and deeper observability for batch ML pipelines.

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