Optimizing the efficiency of your AI and cloud computing is essential if you want to stay ahead of the competition while also driving business growth, productivity and security without increasing your costs and resource use.members dive into the latest technologies and best practices you should consider implementing.
One best practice for enhancing AI is implementing data-driven reports with full traceability, educating all employees on the importance of data quality and fostering a data-first culture over time. This approach ensures data sufficiency for effective AI usage, transforming organizational decision-making and enhancing strategic alignment and efficiency. -
Like past technological waves, AI is setting up to be a race to the middle. That has played out with the cloud, and now we get less from each cloud investment, as people are overwhelmed by how much we've thrown at them. Leveraging unified versus point solution-based AI that sits on top of our cloud software can improve the efficiency of our cloud investments and ensure we get durable differentiation from AI.
AI is a diverse technology with many applications that will entail many different deployment options that will be constantly changing as edge AI and cloud AI continue to evolve. Tech leaders should segment AI applications and workloads based on the cost and determine where they are best run on-device, across edge infrastructure and cloud. -
Large language model operations is an emerging category that can help provide for more efficient and optimized use of LLMs. Running these models can be expensive, which can make it challenging to achieve sufficient ROI as well as scale. LLMOps are essentially a framework that focuses on efficient resource allocation, tracking models, evaluating responses and improving inference. -