Developing an automated training pipeline primarily serves the purpose of ensuring that the model can be retrained in the future. An automated training pipeline is designed to streamline the process of data ingestion, preprocessing, model training, evaluation, and deployment. This is particularly important in dynamic environments where models need to be updated regularly due to changes in data patterns, new incoming data, or evolving business requirements.
By establishing an automated pipeline, teams can efficiently schedule regular retraining sessions, implement updates seamlessly, and maintain consistent performance from their machine learning models. The automation reduces manual intervention, which can lead to inconsistencies and delays in the retraining process. This is beneficial for keeping the models relevant and accurate over time, ultimately supporting better decision-making and outcomes.
Other options focus on different aspects of machine learning pipelines but do not directly relate to the main benefit of enabling future retraining, which is critical for maintaining model efficacy in real-world applications.