ATD-2 Machine Learning Model Validation Framework Design

To realize the full power of machine learning, organizations need to focus on operations and delivery of the predictions as much as the development of the algorithm itself. In the case of NASA and ATD-2, not being able to trust the projections generated could be a life-or-death situation. Air traffic controllers must trust the recommendations presented by them, and validation is essential towards building trust.

ATD-2

Mosaic ATM Case Study | ATD-2 Addressing Inefficiency in Today’s Air Transportation System According to NASA1, many of today’s air transportation system issues can be attributed to a lack of information sharing amongst the operators responsible for managing air traffic in busy terminal environments. Concepts and technologies to improve the Read more…

Similarity Learning for Image Geolocation

We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.

Moving Fast & Deploying Predictive Analytics

Working in conjunction with subject matter experts, data scientists can swiftly apply statistical tools and uncover emerging trends. This is extremely valuable for companies trying to operate in a disruption. Not only will executives have an accurate representation of their present situation, but new products & services can be devised from these insights.