SensiML debuts Data Studio to transform AI/ML sensor data management
SensiML Corporation, an IoT AI software provider and a subsidiary of QuickLogic, has launched Data Studio, a platform that aims to redefine sensor data management. According to the company, this solution facilitates engineers and data scientists by simplifying AI engineering projects, specifically generating datasets for developing and evaluating ML models.
SensiML notes that Data Studio aims to enhance productivity and streamline dataset management for machine learning projects involving sensor data. Notable features encompass real-time connectivity, intuitive visualization tools and collaboration support.
Approximately 80% of the time spent on machine learning (ML) projects is dedicated to data preparation, according to Cognilytica, an AI and ML analyst firm providing research on cognitive technologies. These tasks include data identification, aggregation, cleansing, labeling and augmentation.
Chris Knorowski, the CTO of SensiML, states: “SensiML Data Studio makes sensor data management and analysis more accessible and efficient, empowering developers to build better, more impactful applications using sensor data across a wide range of industries.”
The company says its tool empowers users to capture, import, analyze and label sensor data, enabling valuable insights. The platform supports various collaboration and labeling methodologies, including manual, AI-assisted, and custom options, while also allowing for video synchronization to facilitate complex labeling tasks.
Users can store and analyze data locally or remotely, the company adds. The tool facilitates data analysis and model evaluation through visual comparison of ML models, filtering, transforming and fusing sensor data, all with built-in tools and Python expertise, the company notes. It also provides label and data versioning for tracking labels and model results, with project export to an open format.
SensiML says its technology suits researchers, engineers and data scientists in various sectors, including agriculture, consumer wearables, medical devices, smart buildings and factory maintenance.
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