Project
AWS managed machine-learning platform offering training jobs, real-time inference endpoints, Studio/JupyterLab notebooks, and managed MLflow tracking.
Amazon Payments' contextual bandit lifted conversion by a high single-digit percentage for one customer group in a seven-week test and left another flat. Its team says the content options were the problem, not the LinUCB model choosing between them.
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Amazon's concurrency sweeps push rising traffic at a SageMaker inference endpoint and report where throughput stops improving. Three vLLM settings in the sample deployment decide whether that curve transfers to your traffic.
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Posit's IDE now ships as a custom SageMaker Studio image, and queries to Athena, Glue and S3 run on the Space role's permissions while Posit Assistant authenticates to Bedrock with the same environment credentials.
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A new AWS walkthrough moves a three-backend healthcare agent off ECS with Fargate by wrapping the same code in a runtime decorator. It changes the reasoning model in the same step, so the two versions cannot be diffed for cost.
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AWS counted 13 SageMaker inference launches so far in 2026. The one that changes production behaviour most is a prioritized list of up to five instance types, each allowed its own model optimization settings.
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AWS says both coding agents it tested defaulted to Text Generation Inference and billed GPU time for each crashed deploy before pivoting to vLLM. Its answer is six editable skill files the agent reads on demand.
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AWS's synthetic hazard pipeline reports up to 160 percent higher person-detection mAP50 from images one model edited and a second model labeled. How starved the baseline was decides what that multiplier is worth.
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Training and processing jobs can now name up to five acceptable instance types in priority order. AWS launches on the first one with capacity. Whether that helps you depends on whether your container runs the same on all five.
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AWS shows three agents sharing one AgentCore container across two hosting paths. The orchestration framework absorbs the split; the OpenTelemetry instrumentation does not.
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The richer sync carries training metrics, evaluation results and lineage into the Model Package version so a reviewer can validate from the registry alone, but none of it happens until someone sets AutoModelRegistrationEnabled.
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The discovery sample walks the Identity Center instances in an organization and writes assignments to a queryable store plus CSV, which covers three of the four questions AWS poses and leaves last access to something else.
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Co-hosting models on shared GPUs cut Salesforce's inference infrastructure bill eightfold, but the placement algorithm that made it cheap counted instances and ignored Availability Zones. SchedulingConfig reconciles the two.
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One ModelTrainer and one ModelBuilder replace the SKLearn, PyTorch and XGBoost classes, with source directories synced at job launch. The container is now yours to supply.
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