Monthly summary for 2026-03 (UKGovernmentBEIS/inspect_ai): Delivered a targeted enhancement to the SageMaker provider by enabling log probabilities in completions-style requests for CPT/base models, accompanied by documentation updates. Performed targeted codebase refinements to address minor issues and ensure smooth integration.

February 2026 monthly summary focusing on delivering high-impact features for AWS SageMaker-based model evaluation and inference. The work centered on two repos: aws-samples/amazon-nova-samples and UKGovernmentBEIS/inspect_ai, with emphasis on business value, code quality, and scalable benchmarks.

In Sep 2025, delivered a targeted feature to support custom Lambda-based evaluation in SageMaker Nova recipes for aws/sagemaker-python-sdk, enabling evaluation blocks to specify a custom Lambda ARN, extracted from processor config, and passed as eval_lambda_arn hyperparameter to PyTorch estimators and related utilities. This enhances evaluation flexibility, enabling serverless customization, faster experimentation, and better alignment with Lambda-based workflows. Implemented end-to-end changes with accompanying unit tests to ensure correctness and regression safety, contributing to more reliable and scalable evaluation pipelines and reducing manual configuration overhead.