May 2025: Focused on maintaining cuML Forest Inference demo notebook compatibility with the cuML FIL API changes introduced in version 25.06. Implemented API modernization by replacing deprecated parameters 'algo' and 'output_class' with 'layout' and 'is_classifier' in both direct model loading and Dask worker initialization sections. Result: demo notebook up-to-date and compatible with the latest library, reducing upgrade friction and enabling consistent experimentation for users.

February 2025: Delivered performance-focused features across cuML and docker to accelerate large-scale ML workloads and improve portability. Key outcomes include GPU-accelerated Random Forest wrappers enabling cuML-based training/inference for large datasets, and NCCL performance optimization on ARM with CUDA 12.8. No major bugs reported this month; interoperability and portability improvements were completed as part of feature work, contributing to easier integration with scikit-learn workflows and more efficient distributed training on ARM. Technologies demonstrated include cuML, scikit-learn integration wrappers, CUDA/NCCL tuning, ARM optimizations, and distributed GPU training. Business value includes reduced training/inference time for large datasets and improved scalability across architectures.