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Implementing predictive scaling with AWS SageMaker AutoML

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(@tyler.foster787)
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Cool take! Our approach was a bit different using Elasticsearch, Fluentd, and Kibana. The main reason was automation should augment human decision-making, not replace it entirely. However, I can see how your method would be better for larger teams. Have you considered feature flags for gradual rollouts?

Feel free to reach out if you have more questions - happy to share our runbooks and documentation.

The end result was 99.9% availability, up from 99.5%.

Additionally, we found that documentation debt is as dangerous as technical debt.


 
Posted : 07/12/2025 7:34 am
(@deborah.cook920)
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Allow me to present an alternative view on the timeline. In our environment, we found that Elasticsearch, Fluentd, and Kibana worked better because security must be built in from the start, not bolted on later. That said, context matters a lot - what works for us might not work for everyone. The key is to start small and iterate.

Additionally, we found that starting small and iterating is more effective than big-bang transformations.

For context, we're using Vault, AWS KMS, and SOPS.


 
Posted : 07/12/2025 10:05 pm
(@maria.turner939)
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We went through something very similar. The problem: scaling issues. Our initial approach was ad-hoc monitoring but that didn't work because it didn't scale. What actually worked: drift detection with automated remediation. The key insight was failure modes should be designed for, not discovered in production. Now we're able to detect issues early.

One more thing worth mentioning: unexpected benefits included better developer experience and faster onboarding.

I'd recommend checking out conference talks on YouTube for more details.


 
Posted : 09/12/2025 1:58 am
(@joan.hill519)
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Appreciated! We're in the process of evaluating this approach. Could you elaborate on the migration process? Specifically, I'm curious about stakeholder communication. Also, how long did the initial implementation take? Any gotchas we should watch out for?

I'd recommend checking out the community forums for more details.

Feel free to reach out if you have more questions - happy to share our runbooks and documentation.

Additionally, we found that starting small and iterating is more effective than big-bang transformations.


 
Posted : 13/12/2025 3:54 am
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