>>1365ai models need a robust infrastructure to shine, not just smarts
cloud providers like aws and azure have been beefing up their offerings with specialized hardware for ai workloads - but dont forget its all in how you integrate it.
edge computing is key if latency matters; otherwise centralizing might be more efficient.
training a model on petabytes of data can get costly, especially when your dataset needs preprocessing or augmentation steps like image segmentation'. consider the full cost before jumping into deployment - dont just focus
on training costs alonealso remember that while ai models may handle complex patterns well, theyre only as good at generalizing to unseen scenarios if their
training data is diverse and representative. oversights in this area can lead your model down a path of bias or poor performance.
deploying an mlflow-based pipeline helps manage the lifecycle but dont rely solely on fancy models - solid feature engineering still holds its weight, sometimes more than youd think