Home > Servers > Modular Servers > White Papers > Reference Architecture: Machine Learning Containers on PowerEdge MX and VMware Cloud Foundation 4.0 with Tanzu > From Jupyter notebook to production cluster
As previously mentioned, a lot of work-related to data science happens on engineers’ laptops using Jupyter Notebooks. This creates a custom environment difficult (or impossible) to move into production.
In the case of data science platform services from cloud service providers such as Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP), these are predominantly “outsourced” Jupyter Notebooks with custom integration to their ecosystem (plugins). This allows them to deal with problems on a certain level, which is hard to replicate on-premises and on another cloud provider. Being cloud-agnostic and avoiding vendor locks are very important and a feature in the proposed data science platform solution using Kubeflow.