Unlike other years, building an artificial intelligence model is now simple for developers using well-defined architectures, pre-trained AI models, and a wealth of training resources. Developers can build trained models with accurate capabilities in the lab.
But implementing these same trained models in the real world is extremely difficult. Trained models perform differently when transitioning from the lab to production. Factors like inconsistent data, latency, insufficient compute resources, and variable user-specific performance will impact the performance of a trained model.