Our NEXTOps℠ Framework streamlines and scales to keep things modern and eliminate technical debt
Situation analysis
In today’s fast-paced and evolving development environment, manual processes that churn out software deployment, machine learning, software integration, and data engineering a...
Google Cloud recognizes NT Concepts’ technical proficiency and proven success in machine learning in the federal sector
FOR IMMEDIATE RELEASE
Vienna, VA, March 30, 2020— NT Concepts today announced that it has achieved the Machine Learning Partner Specialization in the Google Cloud Partner Ad...
This article was originally published in Intelligence Community News on February 3, 2020.
Enterprise-scale data management and engineering best practices in support of advanced analytics
The Intelligence Community (IC) has placed the highest priority on the continuous delivery of valuable a...
This article was originally published in Intelligence Community News on December 02, 2019.
Deploy machine learning at the enterprise level in your agency by combining it with DevSecOps
Machine learning is a game-changer for the Intelligence Community. Unfortunately, many agencies only apply...
Federal agencies understand that the health of their large-scale equipment is mission-critical. From fleets of machinery to military installations to aerospace, success often depends on ensuring the mission is not impacted by costly asset downtime and extending the life of the assets supporting...
Moving from paper to digital to enhance the mission
Government agencies have begun to make significant strides in their paperless journey. NT Concepts performs in the customer’s reality, then collaboratively designs and implements solutions like Robotic Process Automation (RPA) and Artificial ...
It is widely accepted that human cognition is fallible and vulnerable to a wide range of cognitive biases. One could even argue that motivation for incorporating Machine Learning (ML) into business processes is to help mitigate some of these biases. ML algorithms, however, are like any other te...
If you haven’t read through the first three quadrants of Machine Learning (ML) Lifecycle series yet, we encourage you to take a moment to familiarize yourself with the building blocks of AI/ML project design, data preparation, and model fitting.
Once the team reaches the Inference and Deploym...
Even though the bulk of the work happens during the data preparation and labeling phase in quadrant two, model fitting constitutes the meat and potatoes of Machine Learning (ML). This third phase of the ML Lifecycle is iterative and slow—a full training run may take days or even weeks. Early vers...
The large volume of available data has spurred a surge in Machine Learning (ML) projects. In the first blog post in the Hidden Challenges of Machine Learning Lifecycle series, we looked at the complexity of clarifying the question to be solved with ML and the process of designing the project, inc...
