The feature store has become a hot topic in machine learning circles in the last few months, and for good reason. It addresses the most painful challenge in the ML lifecycle: dealing with data, or in other words, feature engineering.
In this article, we will walk you through steps to run a Jenkins server in docker and deploy the MLRun project using Jenkins pipeline.
The feature store has become a hot topic in machine learning circles in the last few months, and for good reason. It addresses the most painful challenge in the ML lifecycle: dealing with data, or in other words, feature engineering.
Fight first-day churn with a data science platform that enables rapid deployment of a real-time operational ML pipeline at scale.
Extend Kubeflow’s functionality by enabling small teams to build complex real-time data processing and model serving pipelines.
Data Science Salon wrapped up last week, with tons of insightful talks on a large range of topics in the Media, Arts and Entertainment industries. Check out our roundup of some noteworthy sessions.
How data engineers can leverage ML pipelines to support complex data management tasks across multiple compute environments, bringing ML applications to production faster and easier.
A step by step tutorial on working with Spark in a Kubernetes environment to modernize your data science ecosystem