Ensuring AI/ML Pipeline Resilience: Best Practices for Model and Data Backup

By Aniket H. Kulkarni, VP of Engineering, RackWare

Containerized AI and ML workflows have become the backbone of modern data science, enabling teams to build, train, and deploy models with greater speed and consistency. However, these pipelines often rely on ephemeral compute resources and external storage, making long-running training jobs and fragile model checkpoints vulnerable to unexpected failures.

What is at risk?

• Week-long training jobs on spot instances being preempted mid-run

• Hardware or node failures wiping out local volumes and feature stores

• Tight deadlines turning small interruptions into days of catch-up work

• And many more…

Traditional backup approaches and their drawbacks:

• Rsync scripts and cron jobs that must run at precise times

• Cloud provider snapshots that require manual coordination

• In-application checkpointing that slows down experiments

These manual practices struggle to keep pace with rapid iteration. Frequent checkpoints can slow down experiments, while infrequent backups risk significant data loss. Migrating pipelines between environments – on-premises to cloud, or between regions – often feels like starting from scratch as configurations, secrets, and persistent volumes need custom handling.

Rackware SWIFT offers a unified, agentless solution that bridges these gaps. By replicating CSI-backed and cloud-native storage volumes along with Kubernetes resources in one coordinated process, SWIFT captures both data and configuration without pausing active workloads or requiring any production YAML edits or relabeling. You can enable backups in production without impacting running pipelines.

How SWIFT can help?

• Agentless, non-intrusive replication that runs alongside your workloads

• Incremental snapshot engine that only transfers changed data

• Policy-driven schedules, retention, and target location configuration

• Point-in-time snapshots for quick restores of model checkpoints and namespaces

• Seamless pipeline migration to any Kubernetes or OpenShift cluster

• Store backups in ANY block or Object-Storage

With SWIFT’s policy-driven schedules, teams define backup frequencies, retention periods, and target locations – whether that’s cloud object storage or a secondary Kubernetes cluster. Each snapshot preserves metadata for single-command restore, reducing recovery from hours or days to mere minutes.

A typical SWIFT workflow:

• Define a policy: snapshot training volumes hourly, sync to remote object store, retain last 48 snapshots

• Monitor sync status via dashboard or CLI with no impact on production performance

• Trigger a restore or migration using ‘sync’ or ‘restore’ commands

By automating backups, enabling point-in-time restores, and simplifying migration, Rackware SWIFT lets AI and ML teams focus on innovation instead of operational drudgery. Request a free SWIFT evaluation today and keep your models – and your momentum – running smoothly!

About the author

Aniket H. Kulkarni is Vice President of Engineering at RackWare, where he leads the development of the SWIFT container migration and disaster recovery solution.