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How to Roll Back After a Failed Model Update?
Short answer
Rollback After a Failed Model Update
A failed model update can lead to unexpected issues that necessitate a return to the previous version. The rollback process should be well-planned and documented to ensure the integrity of the systems.
1. Backup of the Previous Model
Before performing an update, a backup of the previous model should always be created. This backup allows for a quick and efficient return in case of an error. It is important that this backup is stored in a secure location to avoid data loss.
2. Stopping the Current Model
To disable a faulty model, the current model must be stopped. This can vary depending on the system and infrastructure. It is advisable to inform all affected services and, if necessary, make a maintenance announcement to inform users about the downtime.
3. Restoring the Previous Version
After stopping the faulty model, the previous version is restored. This can be done by applying the backup or by accessing a version control system if available. It is important to ensure that all necessary dependencies and configurations are also reset.
4. Testing Functionality
After restoration, the system should be thoroughly tested to ensure everything functions properly. This includes both functional tests and user interface tests to ensure that all features work as expected.
5. Documentation and Logging
The entire rollback process should be documented to ensure transparency and facilitate future rollback operations. Detailed logging of the steps taken can help avoid similar issues in the future and analyze the causes of the failed update.
By following these steps, an effective rollback after a failed model update can be performed, ensuring the stability and reliability of the system.
Key facts
- Rollback Process
- Backup of the previous model, stopping the current model, restoration and testing
Sources
All external claims are backed by traceable sources.-
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Cybersecurity Framework (CSF) 2.0 National Institute of Standards and Technology (NIST)
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Artificial Intelligence Risk Management Framework (AI RMF 1.0) National Institute of Standards and Technology (NIST)