Experimentation
for rapid AI & ML development
Data is the foundation of the AI & ML model development process. Carefully crafted algorithms won’t get off the ground without it & bad data can sink it. Numerous experiments are required to develop effective models that push projects forward. With more experiments & smaller volumes of data, high-performing AI teams can build baselines quickly & then rapidly iterate to improve. This experimentation cycle is key because the business impact of ML is often speculative & multiple approaches must be attempted before proving what works.
This whitepaper shows how more experiments & smaller volumes of data help high-performing AI teams build baselines quickly & rapidly iterate to improve.
