Prioritizing Data in Government Technology Modernization
In government, digital modernization often overlooks enterprise data management, which results in fragmented ecosystems that limit modernization’s full potential.
In government, digital modernization often overlooks enterprise data management, which results in fragmented ecosystems that limit modernization’s full potential.
Governments need AI foundation models that are fine tuned to their specific requirements — for relevancy, accuracy, and effectiveness in their specific use cases.
Deciding where and how to begin using generative AI can be daunting. Here are some straightforward tips for adopting this new technology.
The White House’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence lays out a strategy focused on monitoring, regulating and staffing the development of AI-based innovations. Here are some ways government is meeting its requirements.
Agencies can optimize the performance of their IT systems and applications by taking a comprehensive approach to collecting and analyzing data. Artificial intelligence, and a unified data platform, can help agencies maximize those observability efforts.
Mismanaged data can lead to poor decision-making, loss of trust, increased risk and other fallout, and artificial intelligence has made data use more complicated. Fast, secure, energy-efficient data storage, however, helps agencies manage what they have.
In the journey toward building more equitable and inclusive AI systems, diversity is not just a buzzword — it’s a fundamental principle that must be embraced at every stage of development and implementation.
RPA has been a dominate force in automating and enhancing digital efficiency in government operations, yet the lure of AI threatens its continued use and expansion.
Civic tech is a vital way to improve constituent trust, and there’s a lot of potential for it to develop and grow.
Dive into the heart of MLOps, where data science meets DevOps, ensuring seamless integration of machine learning models into production. Discover the essential practices and tools for automating, monitoring, and managing the entire ML lifecycle.