Explore how to use Dask over Kubernetes when handling large datasets in data preparation and ML training, with code examples and a link to a full demo, as well as practical tips to get you started.
Explore how to use Dask over Kubernetes when handling large datasets in data preparation and ML training, with code examples and a link to a full demo, as well as practical tips to get you started.
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.
With MLOps you can deploy Python code straight into production without rewriting it, saving you time & resources without sacrificing accuracy or performance.
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.