PREDICTING THROUGH AUTOMATED REASONING: A TRANSFORMATIVE PERIOD FOR STREAMLINED AND ATTAINABLE NEURAL NETWORK FRAMEWORKS

Machine learning has advanced considerably in recent years, with systems surpassing human abilities in numerous tasks. However, the real challenge lies not just in creating these models, but in implementing them optimally in everyday use cases. This is where machine learning inference becomes crucial, surfacing as a critical focus for experts and i