The transition from LIBOR to alternative reference rates presents numerous challenges, particularly when it comes to renegotiating and operationalizing fallback language for both existing and new contracts. Although uncertainty remains among market participants and industry guidance continues to evolve, adopting a “wait-and-see” attitude before amending contracts is not considered wise. Financial institutions should consider acting […]
Category Archives: Artificial Intelligence
How MLOps Deliver Machine Learning Applications to Scale Production AI
As you work to build your business into an AI-driven enterprise, you’ve probably encountered a few baffling pitfalls along the way. While you have hired top-notch data scientists to build models and invested in expensive data science tools, you still can’t get your AI projects off the ground. Why? What factors make it so hard […]
Improve Threat Intelligence with Data Analytics and Machine Learning
The cybersecurity industry is increasingly producing enormous amounts of raw threat data. The sheer volume of information threat researchers must sift through makes it difficult to collect, analyze, and research that data on time. This in turn limits their ability to understand what data is valid and useful and whether threat artifacts will result in […]
Real-World Use Cases of AI and ML in the Oil and Gas Industry
Take a look at 10 real-world use cases that demonstrate how AI and ML are already being used in the oil and gas industry, and how AI innovation can help renew the industry. Artificial intelligence (AI) is pretty much the first in line as the key technology with all the potential to unlock a productivity […]
Trust and Understand AI for Successful Transformation with Regulatory Compliance
When considering AI, you may have potential questions or simply want to understand AI better. Trusting and understanding AI is key to a successful AI transformation. This is especially pertinent in industries with stringent regulations, such as financial services, banking, and healthcare. After all, if the AI gets it wrong, people may be adversely affected.
Tackling Clinical Trial Data Overload with Data Lakes and Machine Learning
As clinical trial complexity increases, trial sizes grow, and data variety and volume explode, this problem is only growing worse. For sponsors and CROs who are experiencing this challenge, within and across studies, a clinical data and analytics hub built on big data, data lake architecture offers great promise. This article explores how a data […]