- Why are data science efforts with chromatography data among the most valuable analytical investments you could make? - What are ways for evaluating the cost/benefit of investing in analytical efforts? - How can analytical efforts be phased so that benefits accrue to your scientists/engineers and broader organization? - How do ML projects with respect to physical processes differ from pure data processes? - What are some ways to consider team composition when assembling an interdisciplinary team tasked with automating analysis? If you've ever wondered about these questions - check out our freshly recorded AI for Chromatography Data Systems webinar!
#chromatography analysis is a powerful application for #ai in the wet lab, but getting the data in place is hard. Join Ganymede's team in a live webinar to learn about how to build your data infrastructure and data capture to implement AI models, save scientists time, and improve lab data integrity. Whether you use Ganymede's native chromatography capabilities or build out your own infrastructure, we'll review best practices and #integration methods - both integration as in area under the curve, as well as integration as in connecting systems!