Data & Machine Learning

Statistical Model Building & Predictions

  • TurbineOne
    Data & Machine Learning
  • Website

Technology Introduction

TurbineOne is composed of accomplished engineers from Google, Amazon, Twitter, Databricks, Salesforce, Cloudera, Toyota,, Mozilla, and many others. TurbineOne’s co-founders have contributed to over 90 patents and have built transformative technologies on top of data ranging from earth satellites to sensors at the deepest depths of the ocean. Therefore, leveraging this depth of experience, our data science solutions generally flow through the sequential stages of data pipeline building, data engineering, machine learning and then Artificial Intelligence product integration.

Problem Statement

Over the past two decades, the U.S. military and intelligence services have invested a tremendous amount in data acquisition technologies. Although now a strategic asset, this capability created a new problem. Front-line operators and senior officials now have to wade through too much information without enough actionable intelligence. Whether it’s UAV operators having to manually stitch together video highlights or petabytes being streamed to secure clouds without the basic data labels to make the information more useful, we’ve collectively gone past the point of “trying to find a needle in a haystack”.

Solution Summary

TurbineOne's technology solutions have recently included alert generation, custom categorization from very large data streams, secure redaction, and building direct feeds to data science models. These solutions leverage the following TurbineOne core capability areas:

  • Massive-scale data tagging and labelling
  • Digital Automation
  • Dynamic Model Tuning
  • Search
  • Object Detection
  • Natural Language Processing (NLP)
  • Predictive Models
  • Generative Models

Putting all of this together, here's a representative illustration for how these capabilities can be integrated to improve national security:

This technician receives an on-screen flyover alert saying, "Operator Smith, you just said 'ABC'. Did you mean to say 'XYZ' instead?"