Machine Learning Model Inference vs Machine Learning Training

Machine learning model inference refers to deploying a trained model to a production environment, where it processes live, unseen data to generate results, marking the model's operational stage within the machine learning lifecycle. This phase follows the training phase, which involves data collection, model selection, and optimization by data scientists, ensuring the model's accuracy and generalization capabilities. Unlike training, model inference often requires collaboration with data engineers and IT specialists due to its integration into the broader system architecture, necessitating considerations such as resource management and data flow. Despite their differences, both phases are crucial for a fully functioning model, and understanding these distinctions is essential for efficient model deployment and ongoing monitoring. Seldon is highlighted as a solution for managing the complexities of real-time machine learning deployments, offering flexibility, standardization, and efficiency through its modular design and dynamic scaling capabilities.