MakeML is a comprehensive macOS-based integrated development environment (IDE) designed to democratize the creation of mobile-optimized computer vision models. In the 2026 market, it stands as a critical bridge between raw dataset management and edge-deployment on Apple and Android ecosystems. The technical architecture focuses on the 'Edge-first' paradigm, ensuring models are optimized for hardware-specific accelerators like Apple's Neural Engine (ANE) and Google's Tensor G-series. MakeML abstracts the complexities of deep learning frameworks such as TensorFlow and PyTorch, offering a streamlined UI for object detection and instance segmentation (Mask R-CNN). It provides a proprietary cloud-training cluster that allows users to offload heavy compute tasks, while the local macOS client handles sophisticated data labeling and augmentation. By 2026, MakeML has expanded its capability to support real-time object tracking and 3D object detection, positioning itself as a primary choice for AR developers and industrial IoT engineers who require high-performance, low-latency vision models without a dedicated Data Science team.
Enables pixel-level instance segmentation for complex shape identification.
Industrial-grade end-to-end MLOps platform for hyper-scale deep learning and GenAI production.
Build, run, and manage AI models at scale with an enterprise-grade collaborative data science platform.
The enterprise-grade studio for foundation models, generative AI, and machine learning.
The engineer's choice for developing, testing, and deploying high-performance AI models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Automatically extracts frames from video files and enables temporal object tracking for faster labeling.
A curated marketplace of pre-labeled datasets ready for transfer learning.
Compiles models specifically to leverage Apple's ANE (Apple Neural Engine) via CoreML.
Abstracted Kubernetes infrastructure for training YOLO and SSD models on NVIDIA hardware.
Uses pre-trained weights to suggest labels for new data entries.
Unified pipeline for generating models compatible with both iOS (CoreML) and Android (TFLite).
Identifying and tracking ball movement and player positioning in real-time on mobile devices.
Registry Updated:2/7/2026
Deploy to iOS app via CoreML
Farmers need an offline tool to identify crop diseases in remote areas.
Counting warehouse items quickly via a mobile camera.