Why Use Kubernetes for Distributed Inferences on Large AI/ML Datasets

Too Long; Didn't Read

Distributed inferences are typically carried out on big datasets with millions of records or more. A cluster of machines equipped with deep learning capabilities is necessary to process such enormous datasets on time. Through the use of job parallelization, data segmentation, and batch processing, a distributed cluster can process data at high throughput. However, establishing a deep learning data processing cluster is difficult and this is where Kubernetes is helpful.