Teledyne FLIR Releases Prism AIMMGen with Synthetic Data Generation
An AI modeling and generation service significantly reduces the cost, engineering effort, and time-to-market for AI-powered products.
AIMMGen is the latest addition to the Teledyne Flir Prism software family. It is an ITAR-free AI model generation service that enables the automated creation of artificial intelligence (AI) and machine learning (ML) models using synthetically generated data. It offers significant cost and schedule savings for system integrators developing AI/ML products for commercial, first response, and defense applications. The manual labeling process for AI/ML products is highly intensive, and this is further exacerbated when it includes rare and arbitrary objects such as military target classes for which data collection in real-world scenarios is both costly and difficult if not impossible. Prism AIMMGen overcomes these hurdles with a synthetic data generation toolchain capable of generating millions of ML-ready examples of arbitrary objects in various environments and weather, all in both visible light and infrared spectrums. Generating annotated synthetic imagery in minutes that can be used to fine-tune AI models significantly reduces engineering efforts and development timelines. “With the pace and sophistication in which systems are developing, it is critical to leverage smart, synthetic data to train AI models so they are best-in-class,” said Dan Walker, vice president, Teledyne Flir. “AIMMGen speeds developers' time to market while ensuring that their respective AI products are ready for whatever scenario they might encounter in practically any environmental condition.” The automated AI optimization system produces high-quality models without costly data collection. Its inhouse ML operations (MLOps) infrastructure ensures data safety and leverages an extensive synthetic data lake and decades of expertise. Cost-effective development enables the release of a new model in response to new task requirements, target classes, or fine-tuning within days, future-proofing the model as the mission evolves.