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...
As a Ph.D. computer and data scientist, systems and software engineer, applied statistician, and engineering fellow, I have worked on multiple large-scale Artificial Intelligence (AI) and Machine Learning (ML) projects for more than 30 years. Through that extensive experience, I have seen that cr...
Artificial Intelligence (AI) and Machine Learning (ML) are a thing. They are here and here to stay. The NT Concepts Data Science Team is hard at work applying differentiating methods and models to complex data sets for our federal customers. We try our hands at everything from computer vision and...
