PREDICTING VIA AI: A DISRUPTIVE PHASE FOR STREAMLINED AND ATTAINABLE NEURAL NETWORK ALGORITHMS

Machine learning has made remarkable strides in recent years, with systems matching human capabilities in numerous tasks. However, the true difficulty lies not just in developing these models, but in implementing them effectively in practical scenarios. This is where machine learning inference takes center stage, arising as a primary concern for re