In the race to operationalize AI, success depends not on flashy pilots, but on turning experimentation into measurable business value. According to David Ellison, Chief Data Scientist and Director of AI Engineering at Lenovo, the most successful AI projects start with clear business outcomes—not models. From cost savings to new revenue streams, the focus is on impact, supported by infrastructure that can scale and systems that users trust.
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Neural networks news
Intel NN News
- KVCrush: Rethinking KV Cache Alternative Representation for Faster LLM Inference
Developed by Intel, KVCrush can improve LLM inference throughput up to 4x with less than 1% […]
- Scaling AI with Confidence: Lenovo’s Approach to Responsible and Practical Adoption
In the race to operationalize AI, success depends not on flashy pilots, but on turning […]
- Unlocking AI-Driven Media Monetization with Intel® Xeon® CPUs and Broadpeak BannersIn2
In this article, we will cover how to deploy high-performance AI inferencing for media data […]
- KVCrush: Rethinking KV Cache Alternative Representation for Faster LLM Inference
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