This learning exercise delves into the end-to-end process of building and managing Machine Learning (ML) pipelines using the OpenShift AI. Through a highly structured guide, developers and platform engineers are led through each step required to construct an efficient pipeline. Beginning with data acquisition, the pipeline navigates seamlessly through model training, performance evaluation, and culminates in the storage of the trained model in Amazon S3. Furthermore, the exercise empowers developers to automate pipeline runs, streamlining the entire ML workflow.
In the domain of machine learning (ML), the proficient management of trained models is essential. OpenShift AI Pipelines offer a robust solution to automate and streamline the ML lifecycle. This exercise delves into crafting a customized component for OpenShift AI Pipelines, elevating the efficiency of model creation and management within your pipeline options provided by OpenShift AI.
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