Table of contents

Administering Watson Machine Learning

You can manage the tools and services that are available with the Watson Machine Learning service.

Scaling Watson Machine Learning based on workload

IBM Cloud Pak for Data supports scaling of add-on services.If you are a cluster administrator, you can scale Watson Machine Learning after installation from small (the default configuration) to medium to manage a larger workload.

Scaling changes the capacity of services by adjusting the number of pods that are available. Pods act as servers with a set resource limitation that run an application or function. Pods consume resources such as core and memory when components distribute tasks to them. Scaling the pods to medium, for example, increases the processing capacity of the application.

For details on scaling, see Scaling services

TLS encryption support

Transport Layer Security (TLS) encryption can help protect Web applications from attacks such as data breaches, and DDoS attacks. TLS-protected HTTPS is quickly becoming a standard practice for websites. Watson Machine Learning service supports TLS encryption .

All communication facilitated within the Cloud Pak for Data environment, and back to the Watson Machine Learning service, is securely encrypted with robust IBM technology, and SSL/TLS encryption by using standard protocols.

For information on using a TLS certificate, see Using a custom TLS certificate for HTTPS connections.

Configure Watson Machine Learning Accelerator for Deep Learning Experiments

Deep Learning Experiments allow you to accelerate the iterative process of training a deep learning model by simplifying the process to train models in parallel with an on-demand GPU compute cluster. In order to build, train, and deploy deep learning experiments, you must configure Watson Machine Learning Accelerator to work with Watson Machine Learning. For details, see Watson Machine Learning Accelerator

Backup and restore

Backing up the Watson Machine Learning service means creating snapshots of persistent volumes that are used by the service to preserve configurations, running jobs, and build artifacts. See Backup and restore for more information.