As technology continues to revolutionize the public sector, artificial intelligence (AI) emerges as a powerful tool for government agencies to enhance efficiency, streamline processes, and deliver improved services to citizens. In this informative webinar, leading AI MLOps vendors will showcase their innovative solutions tailored specifically for government organizations. Join us to explore the cutting-edge AI technologies and applications tha...

Artificial Intelligence (AI) is paving the way for the government to improve citizen services and automate internal use cases such as fraud detection, cyber threat detection, predictive maintenance, benefits & claims processing, border enforcement, and more. A McKinsey study last year found it takes an average of 9-12 months to get a single AI app from idea to production - 80% of those are shelved. According to a Gartner survey, less than...

H2O.ai is helping government agencies with getting artificial intelligence capabilities into the hands of operators and analysts that will enable them to achieve mission outcomes faster, better, and smarter. Our tools empower data scientist teams to develop machine learning models quickly, accurately, and responsibly and then make those capabilities available to their own organizations. Join us for this webinar and learn how government agenci...

Despite the clear value potential of AI, many business stakeholders are frustrated with the slow pace of taking AI projects from proof of concept to actual implementation. Join us on January 11th at 11am CET to learn how to accelerate your AI projects by using a combination of H2O.ai and Qintess technologies that package the framework, people and technology needed to go from use-case identification to project delivery within 3 months only.

In tandem with the rapid adoption and excitement around Artificial Intelligence & Machine Learning, there has been a sub-sector of data science that has been growing at a rapid rate, Responsible AI. One of the early criticisms of machine learning models was that they were inherently "black-box" or too opaque for users to understand why models were coming to the conclusions that they were. The goal of Responsible AI has been to create new m...