Helpful Amazon SageMaker AI Tutorials and Guides
September 28, 2026 · NeuralArmada Team
Most teams ship an LLM feature with no way to collect a verdict on what it produced. This tutorial builds the missing half: a SageMaker Ground Truth private labeling workflow, a human review queue for low-confidence generations, and the plumbing that turns reviewed records into an evaluation set and a fine-tuning set.
Read moreSeptember 21, 2026 · NeuralArmada Team
A working async endpoint for speech-to-text: why real-time endpoints break on long audio, packaging faster-whisper in a custom inference handler, S3 input/output plus SNS notifications, autoscaling down to zero instances, and the failure modes to plan for.
Read moreSeptember 21, 2026 · NeuralArmada Team
Most production ML is still tables, not tokens. Build an Autopilot/AutoMLV2 baseline in one API call, beat it with a tuned XGBoost job using Hyperband, avoid the leakage traps, and deploy the winner behind a serverless endpoint.
Read moreSeptember 21, 2026 · NeuralArmada Team
Most 'we need more GPUs' problems are input-pipeline problems. Three escalating levels of instrumentation for SageMaker AI training jobs - CloudWatch GPUUtilization, in-loop step timing as metric definitions, and a torch.profiler kernel trace - plus the data-loading, batching, and FSDP fixes that recover the wasted GPU-hours.
Read moreSeptember 18, 2026 · NeuralArmada Team
Per-workload tuning only goes so far. Here is the account-level layer: a tag standard IAM actually enforces, cost allocation that reaches Cost Explorer, Studio idle shutdown, a nightly sweep for orphaned endpoints, forecast budgets, and when to buy Savings Plans.
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