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.
Extend Kubeflow’s functionality by enabling small teams to build complex real-time data processing and model serving pipelines.
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.
The average for 1st day churn hovers at 70%. The solution? Predict user retention in the crucial first seconds and minutes after a new user onboards.
Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. MLOps is the solution.
ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.