Backup and restore is the practice of creating copies of data, configurations, and system state that can be used to recover from data loss, corruption, accidental deletion, or system failures. It is the most fundamental data protection mechanism.

What to Back Up in AI/ML Systems

• Model Weights: Trained model files — often tens to hundreds of GB. These represent weeks of compute investment.
• Training Data: The datasets used for training, fine-tuning, and evaluation.
• Configuration: System prompts, model configs, deployment manifests, feature flags, API routing rules.
• Vector Databases: Embeddings and indexes for RAG systems — rebuilding from scratch can take hours.
• Application Data: User conversations, feedback, evaluation results, usage logs.
• Infrastructure-as-Code: Terraform, Kubernetes manifests, CI/CD pipelines, and environment definitions.
• Secrets: API keys, certificates, and credentials (in encrypted backups).

Backup Strategies

• Full Backup: Complete copy of all data. Comprehensive but time-consuming and storage-intensive.
• Incremental Backup: Only backs up changes since the last backup. Faster and smaller but requires the full backup chain for restore.
• Differential Backup: Changes since the last full backup. Middle ground — faster than full, simpler restore than incremental.
• Continuous Backup (CDP): Every change is captured in real-time. Minimal data loss but requires more infrastructure.

Backup Best Practices

• 3-2-1 Rule: Keep 3 copies of data, on 2 different media types, with 1 copy offsite (or in a different cloud region).
• Automated Scheduling: Never rely on manual backups — automate on a schedule (daily for most data, hourly for critical data).
• Test Restores Regularly: A backup that can't be restored is worthless. Test restore procedures at least quarterly.
• Encrypt Backups: All backups should be encrypted at rest and in transit.
• Retention Policy: Define how long backups are kept — balance between recovery flexibility and storage costs.

Cloud Storage Options: AWS S3 (with versioning and cross-region replication), Google Cloud Storage, Azure Blob Storage, all with configurable lifecycle policies and storage tiers.

Backup and restore is the last line of defense against data loss — when everything else fails, recent, tested backups are what save the organization.