DevConf.in 2018 is the second free annual community conference sponsored by Red Hat for Developers, System admins, DevOps engineers, Testers, Documentation writers and other contributors to open source technologies. It is a platform for the local FOSS community participants to come together and engage in the knowledge sharing through technical talks, workshops, panel discussions, hackathons and such activities.
The fabric8-analytics platform has a use case of showing companion recommendations for a users' input stack (manifest) to help the user make better choices around which packages he should/should not include in the stack. Earlier this use case was solved using a classical machine learning approach that worked well for the problem size at the time, however, wasn't suited for large-scale ecosystems like NPM. In order to scale our recommendations, I made use of recent advancements in the area of deep learning for recommender systems to apply a state of the art model for serving companion recommendations. In this session, I'll cover the end to end picture of bringing this machine learning model to production, all the way from data collection to training the model to serve recommendations.
