Novetta is using deep learning to automate advanced analytic pipelines for our customers. These new capabilities identify important pieces of information across large text, image, and video data sets, a challenge faced by many of our Defense and Intelligence Community customers. We developed Argo, a standardized, Docker-based model training and deployment pipeline, to enable rapid evaluation and deployment of state-of-the-art deep learning models. Argo enables models to be deployed as endpoints and, when used with Amazon SageMaker, it forms the foundation for us to deploy, scale, and manage our deep learning models. Simplifying infrastructure setup through Docker and AWS allows…
20% Accuracy Bump in Text Classification with ME-ULMFiT
Introduction In a previous post, we discussed an enhanced method of passing additional data to the ULMFiT1 algorithm for unstructured text classification, which improved accuracy by up to 9%. In follow-on experiments, we have demonstrated that this approach – in which we prepend metadata to the text to provide more information to the model – increased accuracy by up to 20%, in relative terms. This improvement required just a few lines of Python, such that the performance boost comes at essentially no cost. This blog post explains the findings of this more-detailed evaluation, where we have compared how the following…
Training an ML Model on the Show Floor: Rapid Prototyping at AWS PSS
AWS PSS attendees interact with Novetta’s Machine Learning Prototype demo on the show floor. Introduction In our previous blog post, Sharpening the Edge with IoT, ML, and Homomorphic Encryption, we discussed our prototype application with the following functionality: Identify when a simulated license plate (alphanumeric characters on a white card) appeared in a live video stream Extract text from the license plate card using Amazon Rekognition Securely search an encrypted watchlist for this text Return any matches to the user Novetta and Enveil demonstrated this prototype at AWS Public Sector Summit, June 11-12. This blog post is an…
Introducing Metadata Enhanced ULMFiT
Introduction Since its release in 2018, Novetta’s Machine Learning Center of Excellence has evaluated the performance of Universal Language Model Fine-Tuning (ULMFiT)1 against many customer use cases. We have found that automated labeling of unstructured text helps analysts be more efficient, freeing them up to focus on higher-value tasks. We have been so impressed with its accuracy that we have deployed ULMFiT against a dataset in a production system, with plans to expand it to dozens of other datasets. While ULMFiT’s performance has been impressive on all the datasets we’ve tested, we thought it could do more. Our first use…
