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.
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.
Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. MLOps is the solution.
Version 2.8 includes an exciting set of features that help users to build and manage their operational machine learning pipelines. We’ve introduced a new set of functionalities around MLOps which assists in solving some common challenges in bringing AI to production. And this is only the beginning.
Data science has come a long way, and it has changed organizations across industries profoundly. Very reliable systems and automated algorithms are being developed to harness this data to deliver increased efficiency and value to humanity.