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Robert Schwentker
Fintech Education Pioneer & Hackathon Producer * Global Keynote & Panel Moderator
Robert Schwentker is a prominent figure in the high-tech education and innovation industries with over ten years of experience in fintech, education, and innovation. He pioneered the Silicon Valley blockchain and crypto pre-accelerator, Blockchain University, which quickly expanded to include key players in the industry and top universities delivering education, workshops, labs, and hackathons to India and Japan, and produced over 140 YouTube lectures and 60 projects.
Schwentker is a sought-after speaker, educator, event producer, MOOC builder, developer relations strategist, fintech hackathon master, researcher, and lecturer. His extensive experience includes working for Citi, PayPal, Hyperledger, MIT DCI Cryptocurrency Bootcamp for Women & Underrepresented Scholars, UN Women Blockchain Tech Innovation Lab, Mechanics Bank, Hub Ventures Mentor, and Cisco Blockchain Lab Advisor.
Schwentker has global experience, having worked in Canada, Brazil, Belgium, Spain, Italy, The Netherlands, Norway, Turkey, Saudi Arabia, India, Singapore, Venezuela, and Japan. He currently serves as the President of DLT Education and is open to exploring new roles with corporations, startups, and universities.
Schwentker has a Bachelor of Fine Arts in Management from New York University, and he has studied at the Haas School of Business, UCB Center for Executive Education, Stanford University, and UC Berkeley Extension. He has also studied painting, music, and ceramics.
Overall, Robert Schwentker is a seasoned professional in high-tech education and innovation and demonstrates a strong commitment to advancing the blockchain and fintech industries.
Highlights
🚀 Harnessing the Next Generation of AI for Enterprise-Grade Evaluation & Observability 🚀 At the recent LLMOps Micro-Summit, Atin Sanyal, Co-founder & CTO of Galileo, delved into cutting-edge techniques to detect and mitigate hallucinations in Large Language Models (LLMs). His talk highlighted the power of fine-tuning Small Language Models (SLMs) like Luna to provide highly accurate, scalable, and cost-effective solutions for enterprise-level AI applications. Key Takeaways: 🔍 Advanced methods like Chain Pool and Luna can drastically improve the accuracy of LLM evaluations. ⚡ Luna’s innovative approach offers real-time, low-latency results for RAG applications with minimal costs. 🌍 As AI continues to evolve, adopting these robust evaluation techniques will be crucial for maintaining a competitive edge. Stay tuned for our next article in this series: "Building Better Models Faster with Synthetic Data" – where we explore how synthetic data can revolutionize AI model development. 📊 #AI #MachineLearning #GenerativeAI #LLMOps #Innovation #SyntheticData #BusinessStrategy #TechLeadership #DataScience #EnterpriseAI
Unlocking Efficient AI with Turbo LoRA
In the latest GenAI Architecture Series, we explore Turbo LoRA, a breakthrough fine-tuning method pioneered by Predibase. Turbo LoRA not only enhances the quality of AI model outputs but also boosts throughput by 2-3x—solving the typical trade-offs between speed and performance. Discover how this innovation lowers inference costs, reduces latency, and improves accuracy, all in a single adapter that’s easy to implement. The article dives deep into the mechanics of Turbo LoRA and provides actionable strategies for Executives and CTOs to leverage this next-gen technology for smarter, more cost-effective AI deployment.
Read on to learn how to stay ahead in the rapidly evolving AI landscape!
