Chapter 14Reliability Pillar
Although a lot of our discussion of the ML lifecycle thus far has focused on the iterative and experimental nature, once an ML model is deployed into production, or once your ML environments are set up, you will want to ensure that they are resilient and fault tolerant against failures, that they can scale up and down elastically to meet your demands dynamically, and that you can quickly mitigate any ...
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