Kirill Prokofiev developed and standardized machine learning configuration systems for the open-edge-platform/geti repository, focusing on reproducible model training and deployment. Over four months, he introduced manifest-driven workflows, harmonized training parameters, and enabled flexible data augmentation using Python and YAML. His work included upgrading Docker-based environments, aligning dependencies such as PyTorch and OTX, and refining model configuration for tasks like object detection and segmentation. By removing redundant tiling and optimizing augmentation settings, Kirill improved inference performance and deployment reliability. His engineering demonstrated depth in configuration management, containerization, and system integration, resulting in streamlined onboarding and more robust machine learning pipelines.
Month 2025-10 Open-edge-platform/geti: Delivered cross-service OTX/DETR enhancements and environment hardening, plus DEIM tiling removal to unlock batch search improvements on XPU. Key outcomes include consistent OTX versioning (2.6.0) across Dockerfiles/config, optimized DETR augmentation settings, refreshed dependency locks, and a leaner DEIM configuration. Result: more reliable deployments, reproducible builds, and improved inference performance on XPU devices.
Month 2025-10 Open-edge-platform/geti: Delivered cross-service OTX/DETR enhancements and environment hardening, plus DEIM tiling removal to unlock batch search improvements on XPU. Key outcomes include consistent OTX versioning (2.6.0) across Dockerfiles/config, optimized DETR augmentation settings, refreshed dependency locks, and a leaner DEIM configuration. Result: more reliable deployments, reproducible builds, and improved inference performance on XPU devices.
Concise monthly summary for 2025-09 focused on the open-edge-platform/geti repo. Delivered a major feature: flexible data augmentation configurability and training environment upgrades. The work includes enhancements to training manifests, Dockerfile updates to use the training_extensions develop branch, and refactor of configuration tools to support new augmentation types and parameters. This enables more flexible dataset preparation, faster experimentation, and improved production readiness. No critical bugs fixed this month. The changes provide business value by expanding model training automation, improving reproducibility, and reducing setup time for data scientists.
Concise monthly summary for 2025-09 focused on the open-edge-platform/geti repo. Delivered a major feature: flexible data augmentation configurability and training environment upgrades. The work includes enhancements to training manifests, Dockerfile updates to use the training_extensions develop branch, and refactor of configuration tools to support new augmentation types and parameters. This enables more flexible dataset preparation, faster experimentation, and improved production readiness. No critical bugs fixed this month. The changes provide business value by expanding model training automation, improving reproducibility, and reducing setup time for data scientists.
In August 2025, the geti repository delivered targeted dependency upgrades and environment standardization to improve model support, stability, and deployment readiness. Key changes include upgrading OTX dependencies, introducing new DEIM manifests for object detection models (Deim-DFine-L, Deim-DFine-M, Deim-DFine-X), and updating related dependencies for compatibility. Training infrastructure was standardized by updating the XPU Dockerfile to include torchvision 0.22.0 and align with PyTorch 2.7.0, reducing environment drift and dependency conflicts. No major bug fixes were reported in this period; the work focused on technical readiness and business value through improved model support and streamlined training pipelines.
In August 2025, the geti repository delivered targeted dependency upgrades and environment standardization to improve model support, stability, and deployment readiness. Key changes include upgrading OTX dependencies, introducing new DEIM manifests for object detection models (Deim-DFine-L, Deim-DFine-M, Deim-DFine-X), and updating related dependencies for compatibility. Training infrastructure was standardized by updating the XPU Dockerfile to include torchvision 0.22.0 and align with PyTorch 2.7.0, reducing environment drift and dependency conflicts. No major bug fixes were reported in this period; the work focused on technical readiness and business value through improved model support and streamlined training pipelines.
July 2025 (open-edge-platform/geti): Delivered standardized AI Task Manifest System with cross-task consistency and tuned training defaults to stabilize and optimize model training. Implemented manifests across anomaly detection, classification, object detection, instance segmentation, keypoint detection, rotated detection, and semantic segmentation, and adjusted default training parameters (max_epochs, learning_rate) for multiple classification and detection models to fix training configurations.
July 2025 (open-edge-platform/geti): Delivered standardized AI Task Manifest System with cross-task consistency and tuned training defaults to stabilize and optimize model training. Implemented manifests across anomaly detection, classification, object detection, instance segmentation, keypoint detection, rotated detection, and semantic segmentation, and adjusted default training parameters (max_epochs, learning_rate) for multiple classification and detection models to fix training configurations.
Overview of all repositories you've contributed to across your timeline