CI/CD for Machine Learning and Data Teams

ML pipelines stress CI in unusual ways: huge dependencies, big artifacts, GPUs, and long jobs.

Data and ML workloads are heavier and slower than typical app CI. The cost and reliability trade-offs are sharper.

Tame heavy dependencies

Cache conda/pip environments and prebuilt wheels; native builds (NumPy, Torch) are slow and flaky to compile each run.

Big artifacts and datasets

Use artifact storage and caching carefully - large uploads and disk-fills are common failure modes.

Memory and timeouts

Training and large data steps OOM or hit timeouts. Right-size runners; self-healing retries transient OOM/timeout automatically.

Cost control

Long jobs on premium runners are expensive. Managed runners at lower per-minute cost plus recovered re-runs cut the bill meaningfully.

Key takeaways

• Cache heavy native deps.
• Plan for big artifacts and disk.
• Right-size for memory; self-heal OOM/timeouts.

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