Helpful Amazon SageMaker AI Tutorials and Guides
September 17, 2026 · NeuralArmada Team
Allocation, lending, borrowing, and preemption on EKS-orchestrated HyperPod: design team quotas, create scheduler configs and compute quotas, submit governed tasks, make preemption survivable with checkpoints, and review utilization instead of allocation.
Read moreSeptember 16, 2026 · NeuralArmada Team
Keep the model on your own SageMaker AI endpoint and put a managed policy layer in front of it: a versioned Bedrock guardrail, input/output screening that respects PII redaction, streaming-safe checks, a CI test suite, and the metrics to watch.
Read moreSeptember 15, 2026 · NeuralArmada Team
Deploy Qwen2.5-VL on a SageMaker AI endpoint for invoice and form extraction: page rendering that does not lose small print, schema-guided JSON decoding, a field-level eval harness that separates misses from confident errors, and a backfill that does not pay for idle GPUs.
Read moreSeptember 14, 2026 · NeuralArmada Team
Put the serving side of SageMaker AI under version control: GitHub OIDC instead of stored AWS keys, a Terraform stack for model, endpoint config, canary rollout and autoscaling, a deploy workflow gated by environment approvals, and nightly drift detection.
Read moreSeptember 14, 2026 · NeuralArmada Team
Quantization has a floor; distillation does not. A full loop on SageMaker AI: deciding whether a task is distillable, generating teacher labels with batch transform, curating the set, LoRA-tuning a 3B student, gating the swap on a sealed eval set, and serving it 10-20x cheaper with a teacher fallback.
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