Mosaic Data Science deploys MLOps to streamline and automate the end-to-end lifecycle of machine learning models, helping firms develop, deploy, monitor, and maintain them. Our success stories bring concepts from software development and DevOps into the realm of machine learning to improve the reliability, scalability, and manageability of machine learning projects.
Mosaic Data Science will be participating in the upcoming American Health Law Association (AHLA) conference on “The Complexities of AI in Health Care.” The event aims to dissect the intricate relationship between AI technology and healthcare, focusing on the critical issue of algorithmic bias and fairness.
Mosaic Data Science is proud to announce its participation in the U.S. AI Safety Institute Consortium (AISIC) through our collaboration with EBG Advisors, the consulting arm of national law firm Epstein Becker Green.
The “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence” Executive Order underscores the need for responsible AI use to mitigate potential risks in the face of recent rapid advances in artificial intelligence, such as the launch of ChatGPT and other generative AI tools that have accelerated a global movement to regulate U.S. tech giants. Mosaic Data Science’s GenAI Readiness Assessment was specifically designed with the latest LLM hype in mind, packaging our expertise in using these models into a robust evaluation of use cases that can benefit from ethical AI implementation.
Mosaic Data Science and Epstein Baker Green discuss the importance of developing ethical algorithms that comply with anti-discrimination laws.
Mosaic supports Epstein Becker Green by deploying AI to support the law firm’s algorithmic bias auditing & risk management services.
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.
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.
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?