This week on The Data Stack Show, Eric and Kostas chat with Tony Wang, Graduate Research Assistant (PhD) at Stanford University. During the episode, Tony discusses his journey from China to studying electrical and hardware engineering at MIT, his transition to data processing systems for his Ph.D., and the academic-industry connection. Tony shares insights on cloud data processing, the limitations of academic hardware projects compared to industry giants like NVIDIA, and the potential for software innovation in academia. He also delves into his current research focus on time series data management, the challenges of integrating different data systems, the goal of improving data processing efficiency, the sales aspect of his research, and more. 

This week on The Data Stack Show, Eric and Kostas chat with Peter Chapman, Peter is a consultant who specializes in helping PLG companies drive more revenue with data. With a background in data and revenue operations, Peter shares his experiences in building data stacks at startups like Heroku, emphasizing the early consideration of data architecture to avoid future issues. He highlights the significance of a cohesive data stack for product-led growth companies and the unique challenges faced by open-source companies in commercializing their projects. The conversation also explores the operationalization of data, the importance of aligning sales with a company’s technical ethos, debating the balance between inference and training costs, the strategic approach to margins by focusing on enterprise features over infrastructure reselling, and more. If you’d like to contact Peter about his advisory services, his email is peter@chapman-coaching.com.

This week on The Data Stack Show, Eric and Kostas chat with Rishabh Bhargava, Co-Founder and CEO of refuel. During the episode, the group discusses the evolution of AI, machine learning, and large language models (LLMs). Rish shares his background and the inception of refuel, which focuses on making clean and reliable data accessible for businesses through data cleaning, labeling, and enrichment using LLMs. The conversation explores the impact of LLMs on data quality, the challenges of implementing LLM technology, and the user experience of working with LLMs. They also touch upon the importance of confidence scores in machine learning and the iterative process of model training, a practical use case involving refuel and RudderStack, and more.