
Ettie Eyre
Platform Engineering Lead
Cookpad
Home / Learn / apply() Conference /
apply(conf) - Apr '21 - 10 minutes
In 2018 we launched an experiment to add machine learning to the ranking algorithms on the social feed of the Cookpad application. The results of this experiment were plausible for our users, however the architecture we built for this experiment did not allow us to scale beyond a limited number of users. Therefore, in our next iteration, we focused on redesigning the architecture to scale to our global user base keeping in mind all the learnings from our first experiment.
In this talk we will discuss why a feature store is essential for serving machine learning at scale. We will describe the feature store solution we have built, its architecture and the pipelines populating the feature store. Finally, we will discuss the optimisations made to our feature store in order to serve data for online inference in our production environment.
Platform Engineering Lead
Cookpad
Machine Learning Engineer
Cookpad
Interested in trying Tecton? Leave us your information below and we’ll be in touch.
Unfortunately, Tecton does not currently support these clouds. We’ll make sure to let you know when this changes!
However, we are currently looking to interview members of the machine learning community to learn more about current trends.
If you’d like to participate, please book a 30-min slot with us here and we’ll send you a $50 amazon gift card in appreciation for your time after the interview.
or
Interested in trying Tecton? Leave us your information below and we’ll be in touch.
Unfortunately, Tecton does not currently support these clouds. We’ll make sure to let you know when this changes!
However, we are currently looking to interview members of the machine learning community to learn more about current trends.
If you’d like to participate, please book a 30-min slot with us here and we’ll send you a $50 amazon gift card in appreciation for your time after the interview.
or