We’d by no means suggest altering strong, well-performing workflows only for the sake of change; “if it ain’t broke, don’t repair it” is a typical folksy idiom for a cause: it’s fairly often the right method.
Nonetheless, there’s a sizeable hole between “fairly often” and “at all times,” and our most irritating days at work usually come about when our time-tested strategies fail to supply our anticipated outcomes or carry out poorly. That is the place increasing our data base actually pays off: as an alternative of getting caught within the psychological equal of a spinning wheel of loss of life, we strive one thing totally different, tinker with our course of, and (in the end) transfer ahead with a brand new resolution.
Within the spirit of embracing recent views, we’ve put collectively a lineup of fantastic latest posts that provide an authentic spin on frequent machine studying workflows. They cowl procedures like drift detection and mannequin coaching and duties starting from picture segmentation to named-entity recognition. Make room in your toolkit—you’ll wish to add these!
Earlier than diving in, a fast replace: in the event you’re on the lookout for different methods to remain up-to-date with our greatest latest articles past the Variable, we just launched several Medium lists that will help you uncover extra nice reads.
