Danny D. Leybzon has worn many hats, and all of them have been related to data. He studied computational statistics at UCLA before becoming first an analyst and then a product manager at the big data platform Qubole. He went on to become the primary field engineer for data science and machine learning at Imply, and now serves as MLOps Architect at WhyLabs, a role he describes as a bridge between technical teams and business needs.

In that role, Danny works on the problems that determine whether machine learning actually survives production: model monitoring, data observability, and the tooling trade-offs behind them, from data profiling architectures to build-versus-buy decisions and platform-agnostic integrations. He has worked to evangelize machine learning best practices for years, speaking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and, most recently, AI observability and data logging.

When Danny is not researching, practicing, or talking about data science, he is usually off doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, and more.