Create, organize, and manage RF data collections for MLOps
MLOps—short for Machine Learning Operations—is the discipline of managing the full lifecycle of machine learning models, from development and deployment to monitoring and continuous improvement. By applying software engineering and DevOps best practices to AI workflows, MLOps ensures that ML models are scalable, reliable, and maintainable in real-world applications. While much of MLOps focuses on automation and infrastructure, it all starts with one essential element: high-quality data.
In the world of RF machine learning, data presents unique challenges. Signals are time-sensitive, often noisy, and can vary dramatically across environments. That’s where RF DataOps comes in. RF DataOps focuses on the collection, curation, labeling, and governance of signal data to support robust and repeatable ML pipelines. For RF applications, this means building systems that can intelligently capture, organize, and refine signal data—ensuring models are trained on datasets that are not only large, but also accurate, diverse, and representative. Deepwave’s AIR-T with AirStack platform is designed to help you orchestrate this process at the edge, making RF DataOps both scalable and efficient.
Signal generation equipment orchestration and control.
Add signal impairments using GPU accelerated computing.
Receive and store SigMF recordings in an on-prem or cloud database.
The AIR-T, powered by AirStack software, is an ideal platform for RF DataOps. It bridges the gap between signal generation, data collection, and intelligent labeling. Unlike traditional setups that require manual coordination between test equipment and capture systems, the AIR-T can directly command and control RF test equipment, triggering it to generate specific signals of interest. Since the AIR-T initiates these transmissions, it inherently knows what was sent—enabling it to automatically label the collected data with precise metadata such as modulation type, frequency, timestamp, and emitter ID. This automation eliminates human error and drastically accelerates dataset generation. Each labeled recording is saved in the standardized SigMF format and may be stored in a structured database, making it easy to search, retrieve, update, and integrate into MLOps pipelines. By orchestrating the entire RF data lifecycle—from generation to labeled storage—the AIR-T with AirStack transforms the traditionally manual and error-prone RF data curation process into a scalable, automated, and repeatable workflow.