Data Science with Weaviate and GraphQL
Weaviate is using GraphQL to provide user-friendly data interaction. Weaviate is a vector search engine, and all search (e.g. semantic, contextual) search is done via its GraphQL API. We've put a lot of thought in the design of the GraphQL API, which results in good user and developer experience. In this talk, we will take you along in the journey of how our GraphQL implementation was shaped according to user needs and software requirements, and show a demo of the current design for Weaviate.
- How to design a GraphQL API from user needs in data science.
- A very practical use case, all open source
- How to use the vector search engine Weaviate with GraphQL
About the speakers
Community Solution Engineer,
I am Laura Ham, community solution engineer at the startup SeMI Technologies. We are developing the open source vector search engine Weaviate, where I am responsible for the design of the technology for its users. Big part my work has been designing the GraphQL API, which I would like to share in this event and hear feedback. Next to working at SeMI, I am active in organizing meetups in the data science and UX field, and I tech coding to kids. I just graduated from my Master's degree in Human Computer Interaction.