Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the focus has shifted toward precision, efficiency, and reliability. Development teams are no longer just building models; they are building complex software systems where the machine learning component is one part of a larger, integrated architecture.
The current landscape is defined by
