Shanti Greene and Benjamin Weinert
Global head of AI and data strategy; senior data scientist at Infocepts; Daugherty Business Solutions
Expertise: Data analysis; programming
Education: UC Berkeley, University of Connecticut, Case Western Reserve University; University of Rochester, Indiana University
Shanti Greene leads AI and data strategy at Infocepts. With over 20 years in the field, he uses advanced technologies like large language models and neural networks to drive business outcomes. Greene’s expertise spans cloud and data engineering, having designed a multi-cloud analytics platform. He also guides AI and 3D printing startups and teaches data science as an adjunct professor at Washington University in St. Louis.
 
Benjamin Weinert, Ph.D. is the data science competency lead and Innovation Hub technical lead for Daugherty Business Solutions. Weinert earned his doctorate in high energy physics, working on the Large Hadron Collider at CERN, where he overcame the shortcomings in theoretical models, which can introduce biases in extracting extremely rare events, by developing data-driven models. With a focus on building pragmatic solutions, Weinert leads multiple data science initiatives, tackling problems such as predictive maintenance, computer vision classification, forecasting, LLMs and anomaly detection. His teams work the entire data science lifecycle, from use case evaluation to monitoring, and span from small proofs-of-concept to production systems. 
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Navigating LLMs can be daunting. Here are some evaluation strategies designed specifically for tech practitioners and business leaders.
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In part three of the series, our experts delve into prompt tuning and its approaches, including hard versus soft prompting.
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When pre-trained large language models don’t cut it for your business goals, fine-tuning may very well be the answer.
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It all comes down to your business’s industry and goals.