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.
-
-
Articles récents
- In-production AI Optimization Guide for Xeon: Search and Recommendation Use Case
- Argonne’s Aurora Supercomputer Helps Power Breakthrough Simulations of Quantum Materials
- Argonne’s Aurora Supercomputer Drives Simulations to Explore How Light Shapes Quantum Materials
- AERIS Earth Systems Model Pushes AI for Science to New Heights
- Leveraging Edge AI for Business Innovation
-
Neural networks news
Intel NN News
- In-production AI Optimization Guide for Xeon: Search and Recommendation Use Case
In this guide, you'll learn multiple aspects of optimizing the Search and Recommendation model […]
- AERIS Earth Systems Model Pushes AI for Science to New Heights
Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory introduce AERIS, […]
- Argonne’s Aurora Supercomputer Drives Simulations to Explore How Light Shapes Quantum Materials
Researchers using the Aurora supercomputer at the U.S. Department of Energy’s Argonne National […]
- In-production AI Optimization Guide for Xeon: Search and Recommendation Use Case
-