Traditional machine learning models like deep neural networks can be highly complex and operate as “black boxes,” making understanding the factors and reasoning contributing to their predictions challenging. Mosaic Data Science uses tools and techniques to make the decision-making process of AI systems transparent, understandable, and accountable to users, developers, regulators, and other stakeholders.
Mosaic developed Explainable AI in Lunar Operations to support lunar rovers using imagery data from hardware to assist NASA rovers in assessing the traversability of a given region of terrain, supporting increasingly autonomous operations.
Mosaic helped a commercial airline integrate custom machine learning to predict departure runway and taxi-out time, cut operational costs, and improve the customer experience, aiding in reducing fuel emissions with AI.
Mosaic built a custom voice search solution using deep learning and state-of-the-art language models to intelligently parse complex mechanical operation manuals and return the desired search results
Mosaic has been working with a leading pet Biotech company on integrating machine learning into the organization’s products, giving pet owners more visibility into the quality of life their pets have by identifying genetic traits that may impact their health or behavior.
Successfully developing machine learning tools to provide value in a business environment requires much more than algorithmic knowledge and tuning. Machine learning cannot provide value in a vacuum.
MLflow is open-source software initially developed by DataBricks for managing the “machine learning lifecycle.” It makes the model artifacts and their environment specifications more readily available when assembling ML model applications or for other purposes such as collaborating with teammates
Traditional lending practices are a prime candidate for machine learning improvements. Lenders can make more accurate and faster decisions by shifting decision-making from analysis of individuals to analysis of trends and patterns.
Algorithms can be trained to mimic human behavior, but what happens when the human developing the algorithm inadvertently allows bias into the training process?
For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.