Modeling Risk Strategy 
CIO Business Intelligence
APRIL 8, 2025
Risk is inescapable. A PwC Global Risk Survey found that 75% of risk leaders claim that financial pressures limit their ability to invest in the advanced technology needed to assess and monitor risks. Yet failing to successfully address risk with an effective risk management program is courting disaster.
O'Reilly on Data
JUNE 27, 2023
Doing so means giving the general public a freeform text box for interacting with your AI model. Welcome to your company’s new AI risk management nightmare. ” ) With a chatbot, the web form passes an end-user’s freeform text input—a “prompt,” or a request to act—to a generative AI model.
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O'Reilly on Data
APRIL 30, 2024
Others retort that large language models (LLMs) have already reached the peak of their powers. It’s difficult to argue with David Collingridge’s influential thesis that attempting to predict the risks posed by new technologies is a fool’s errand. However, there is one class of AI risk that is generally knowable in advance.
Rocket-Powered Data Science
FEBRUARY 15, 2023
While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.
Speaker: William Hord, Vice President of ERM Services
Your ERM program generally assesses and maintains detailed information related to strategy, operations, and the remediation plans needed to mitigate the impact on the organization. In this webinar, you will learn how to: Outline popular change management models and processes. Organize ERM strategy, operations, and data.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
datapine
MAY 31, 2022
More and more CRM, marketing, and finance-related tools use SaaS business intelligence and technology, and even Adobe’s Creative Suite has adopted the model. We discussed already some of these cloud computing challenges when comparing cloud vs on premise BI strategies. The next part of our cloud computing risks list involves costs.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
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