AI Infrastructure
Batch inference processes large datasets through ML models — scoring millions of customer records, generating embeddings for a document corpus, or running image classification on a media library. This OCI-native pipeline uses OCI Data Science for model management, OKE for distributed inference workers with GPU shapes, and OCI Queue Service for job orchestration with checkpointing and failure recovery. Ideal for data science teams running nightly scoring jobs, bulk classification, or periodic embedding generation across large datasets.
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Input data is partitioned in Object Storage and jobs are distributed via OCI Queue Service to OKE workers running on preemptible instances for cost savings. Each worker processes a partition independently with checkpoint writes to NoSQL Database every N records. If a preemptible instance is reclaimed, only the current partition restarts from the last checkpoint. Results aggregate back to Object Storage with OCI Streaming for completion events.
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