Concise monthly summary for 2026-01 focusing on business value and technical achievements across two repos. Key features delivered: - apache/mahout: TensorFlow TensorProto support and encoding into quantum states in QDP. Implemented a TensorFlowReader to parse TensorProto (tensor_content, double_val) and enable encoding of TensorFlow tensors into quantum states, enabling ML-to-quantum workflow integration in QDP. Commits include a reader implementation and encoding tests (two commits total). - NVIDIA/KAI-Scheduler: Refactored JobSet management for replicated jobs and minimum availability with added Kubernetes integration tests to validate the improved JobSet behavior. Commits include changes for follow-suggestions and fixes. Major bugs fixed: - NVIDIA/KAI-Scheduler: JobSet Grouper validation and reliability fixes to ensure code validation and consistency, enhancing reliability of the jobset management system. Commit: chore: apply make validate fixes. Overall impact and accomplishments: - Enabled end-to-end ML-to-quantum data workflows by delivering TensorFlow tensor ingestion and encoding in QDP, broadening ML pipelines within the platform. - Improved Kubernetes JobSet behavior for replicated workloads, reducing scheduling risk and improving availability guarantees. - Increased reliability and maintainability of the jobset subsystem via validation fixes and enhanced test coverage, supporting long-term stability. - Strengthened cross-repo collaboration and traceability through linked commits and test coverage, facilitating easier audits and future enhancements. Technologies/skills demonstrated: - TensorFlow TensorProto parsing and encoding, QDP integration, and test-driven development. - Kubernetes JobSet management, replication logic, and integration testing. - Code validation, make-based quality gates, and cross-repo dependency updates.

2025-12 NVIDIA/KAI-Scheduler monthly summary: Focused feature delivery and integration work around JobSet scheduling, with no major bug fixes reported this period.