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That seems to be trading one type of lock-in for another. The problem with that approach is that now you have added a highly complex layer into your architectur
by BaronSamedi 7y ago
That seems to be trading one type of lock-in for another. The problem with that approach is that now you have added a highly complex layer into your architecture that you need to maintain, integrate, and secure. I think it more likely that in AI, Amazon and Microsoft will continue to add more powerful cloud services making home-grown kubernetes solutions even less relevant.
- streetcat1 7y agoSo, in any case you will have the complexity. With CRD and operators you might be able to automate it away. An AI service is much more than submitting a CSV file to a cloud and getting a model back, or even a web end point. You need: 1) watch for changes in the input data. 2) watch for model decay. 3) watch for latency. 4) retrain (preferably automatically). 5) redesign your pipeline and imputation methods when the data changes. 6) watch for problems in the input data during serving 7) Scale out or Scale down. 8) Be optimal with the training resources (for example, train on spot instances). 9) Canary and shadow release of model 10) Data and model versioning. Not to mention privacy and price. What happen today is that there are no offering of alternative to the cloud services, so there is no point of reference.