How we built a job recommender SaaS with Deep Learning to disrupt the job market!

This talk originates from the archive. To the CURRENT program
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Wednesday, December 5 2018
17:15 - 18:15 | “How we built a Job Recommender SaaS with Deep Learning”. In this talk we’ll tell you about our experience building a job recommender software as a service for VDAB, the Flemish Public Employment Service.

Our deep neural net, called JobNet, “reads” job seeker résumés and job descriptions in multiple languages and learns to embed them in a common space. The resulting embeddings allow us to match job seekers and jobs in both directions.

To deliver the best recommendations at the scale of VDAB, we built JobNet using a modern ML stack based on Dask, Sklearn, and TensorFlow.
We’ll also talk about how we deployed the solution in the cloud using a modern Continuous Integration pipeline based on CircleCI, Terraform, Docker, and AWS ECS.

Behind the Tracks