Founder and CEO

Why do so few hyped AI pilots make it to production? How can projects either fail faster or scale into production? What organization structures and processes enable success when building products with AI components? After many failures and successes building impactful products with AI components, Jacob Miller (VP of Data Science at Pattern) walks through the enduring frameworks and examples to maximize your success. Seating is limited. Register <a target="_blank" rel="noopener noreferrer" class="text-blue-400 underline" href="https://app.siliconslopes.com/events/silicon-slopes-ai-chapter-april-lunch-and-learn">here</a>. Jacob's Bio Jacob’s background and experience joins three perspectives on AI and data science: a) practical day-to-day challenges, b) technical depth, and c) corporate leadership. Over the last 22 years, Jacob has built his expertise and judgment through a career spanning risk and financial modeling, internal consulting, building a data science academic program, and corporate leadership around the world. Since 2020, Jacob has served as Vice President of Data Science at Pattern, where he helped drive the company’s rise as the world’s largest ecommerce accelerator and the top Amazon seller globally. Leveraging AI and machine learning, he played a key role in generating billions in revenue by optimizing and accelerating brand growth. During his tenure, he also authored multiple technology patents, reflecting his innovative approach to applying AI at scale.Lunch Sponsor: <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-400 underline" href="www.pitonventures.com">Piton Ventures</a>Piton Ventures partners with leading AI ventures, SaaS enterprises, and companies leveraging AI for growth. With deep expertise in AI economics and scaling world-class AI products, we bridge the gap between cutting-edge innovation and sustainable business models.

Silicon Slopes AI Chapter April Lunch & Learn 

Why do so few AI pilots reach production? Discover proven frameworks, examples, and lessons from past failures and wins to help your AI projects scale faster and succeed in the real world.