Technical Talks

View All

Rikai: a new data format for analytics on unstructured data at scale

Chang She Chang She | Co-founder and CEO | Eto Labs

Data infrastructure for AI is broken. Repeated conversions, disjoint workflows, and manual QA of labels are just some of the pain-points experienced by any AI team trying to bring models to production. To solve these challenges, we've created a new open-source data format called Rikai. Rikai is designed specifically for AI teams to avoid the need to convert data between ETL, analysis, and training phases of the AI workflow. Using semantic-typing, Rikai also supports deep understanding of AI datasets using plain vanilla SQL. Finally, Rikai already works with existing analytics systems so it fits right into the modern data stack without the need for new compute frameworks or engines.

Chang She
Chang She
Co-founder and CEO | Eto Labs

Chang She is CEO/Co-founder at Eto Labs building modern data infrastructure for AI. Previously he architected the ML and experimentation stack at TubiTV as VP of Engineering. In the mythical pre-pandemic epoch, Chang was the 2nd major contributor to Pandas, CTO/Co-founder of DataPad, and a recovering financial quant.