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.
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.
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 needs to quickly adapt to the fast-paced changes happening all over the world. Currently, many businesses are in a tough spot, and having the right kinds of data and intelligence enables them to react quickly to the unprecedented changes brought about by the pandemic.