Amazon Nova

AWS has released infrastructure for deploying multi-turn reinforcement learning to train Amazon Nova models on SageMaker HyperPod. The system requires a minimum of 10 ml.p5.48xlarge instances and costs approximately $786-$1,180 per hour when running.

AWS has released an automated pipeline for redacting personally identifiable information in images, using Amazon Nova 2 Lite as an intelligent coordinator. The solution combines Nova's contextual vision reasoning with Meta's SAM 3 model deployed on SageMaker and Amazon Textract to handle complex PII detection scenarios including faces, fingerprints, ID cards, and license plates.

AWS published a technical guide showing how Amazon Nova 2 Lite performs object detection through natural language prompts without requiring model training. The multimodal model returns bounding box coordinates in JSON format at $0.0003 per thousand input tokens and $0.0025 per thousand output tokens, with typical images costing approximately $0.00057 to process.

Amazon Web Services released a model distillation pipeline on Amazon Bedrock that transfers video search routing intelligence from Nova Premier to Nova Micro. According to AWS, the approach reduces inference cost by over 95% and latency by 50% compared to using Claude Haiku for intent routing.

Amazon Web Services published a practical guide for fine-tuning Amazon Nova models using the Nova Forge SDK's data mixing capabilities. According to AWS, blending customer data with Amazon-curated datasets preserved near-baseline MMLU scores while delivering a 12-point F1 improvement on a Voice of Customer classification task spanning 1,420 leaf categories.

AWS has introduced Lambda-based reward functions for Amazon Nova model customization through reinforcement fine-tuning (RFT). The serverless architecture automatically scales from 10 concurrent evaluations per second during experimentation to 400+ during production training, supporting both objective RLVR and subjective RLAIF approaches.

Amazon Bedrock now enables fine-tuning of Amazon Nova models using supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and model distillation. The service automates infrastructure provisioning and training orchestration, requiring only data upload to S3 and a single API call. Fine-tuned models run on-demand at standard inference pricing without provisioned capacity requirements.