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The preparation of annotated datasets remains a critical bottleneck in the machine learning (ML) pipeline. Existing tools are fragmented across cloud-hosted services, self-hosted web applications, and lightweight desktop tools—none simultaneously ad-dressing diverse annotation modalities, offline-first operation, integrated training, and serverless collaboration. We present Annotix, an open-source, cross-platform desktop application built on a Rust backend (Tauri 2) and React 19 frontend, designed to unify the entire ML data preparation workflow within a single privacy-preserving environ-ment. To evaluate its practical utility, we conducted a controlled annotation efficiency study using 60 synthetic images (bounding box and mask tasks) annotated by three expert evaluators across Annotix, CVAT, and Label Studio, analyzed via Krus-kal-Wallis with Dunn–Bonferroni post-hoc tests, and a heuristic usability evaluation over standardized tasks on real medical images (retinographies and otoscopies). Re-sults demonstrate that Annotix achieves statistically significant annotation efficiency relative to established tools while offering substantially broader feature coverage, in-cluding 7 image annotation primitives, 19 ML training backends, ONNX-based infer-ence-assisted labeling, and serverless P2P collaboration. Annotix provides a complete, privacy-preserving ML data preparation workflow suited to regulated domains such as medical imaging and ecological monitoring and is freely available under the MIT license.