Your multimodal model achieved 96% accuracy on benchmarks. Your advisor praised it. Conference reviewers loved it. Then you deployed to production and it failed 60% of the time. You're not alone—95% of enterprise AI pilots never leave the lab.

This isn't a technical failure. It's a structural gap between research and production that nobody teaches in grad school. You optimized for benchmark performance, but production demands reliability, cost efficiency, latency, and fairness. These objectives conflict. Your 96% accurate model might be too slow, too expensive, or unfairly biased.

The Research-to-Production Gap reveals why benchmark success doesn't predict real-world viability. Through real examples (IBM Watson's failure, autonomous vehicles reducing catastrophic failures by 73%, healthcare AI achieving 12% accuracy gains), you'll discover how leading researchers master production engineering.

You'll gain concrete strategies for reproducibility, cost optimization, robust data pipelines, and stakeholder alignment—transforming from an academic researcher into an engineer whose innovations actually solve problems that matter.

Stop abandoning projects at the finish line.

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