IBM Data and AI Ideas Portal for Customers


Shape the future of IBM!

We invite you to shape the future of IBM, including product roadmaps, by submitting ideas that matter to you the most. Here's how it works:

Post your ideas

Post ideas and requests to enhance a product or service. Take a look at ideas others have posted and upvote them if they matter to you,

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The IBM team may need your help to refine the ideas so they may ask for more information or feedback. The product management team will then decide if they can begin working on your idea. If they can start during the next development cycle, they will put the idea on the priority list. Each team at IBM works on a different schedule, where some ideas can be implemented right away, others may be placed on a different schedule.

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Some ideas can be implemented at IBM, while others may not fit within the development plans for the product. In either case, the team will let you know as soon as possible. In some cases, we may be able to find alternatives for ideas which cannot be implemented in a reasonable time.

Additional Information

To view our roadmaps: http://ibm.biz/Data-and-AI-Roadmaps

Reminder: This is not the place to submit defects or support needs, please use normal support channel for these cases

IBM Employees:

The correct URL for entering your ideas is: https://hybridcloudunit-internal.ideas.aha.io


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Watson Machine Learning Accelerator

Showing 7

fabric files for both x86 and ppc64le on Same Cluster if HPO Models are running on

https://jazz07.rchland.ibm.com:21443/jazz/web/projects/pc-management#action=com.ibm.team.workitem.viewWorkItem&id=266399Customer will be purchasing more x86 nodes shortly to add to cluster. Customer needs permanent solution
9 months ago in Cloud Pak for Data / Watson Machine Learning Accelerator 0 Future consideration

Power architecture - Pyarrow & RAPIDS latest packages

Please refer the support ticket- TS004247707 for details
11 months ago in Cloud Pak for Data / Watson Machine Learning Accelerator 1 Future consideration

Open to allow WMLA user to add their own algorithms

Our team would like to try WMLA EDT to train model using most up to date, state of the art algorithm. However, these algorithm is not supported by WMLA. It will be best if WMLA allows users, who are data scientist, developer, or machine learning...
about 1 year ago in Cloud Pak for Data / Watson Machine Learning Accelerator 4 Future consideration

Support RBFOpt as one of the hyperparameter optimization algorithms in WMLA

Watson Studio supports RBFOpt off the shelf (see https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml_dlaas_hpo.html), but WMLA doesn't. The inconsistency between both platforms is surprising, and taking into consideration that RBFO...
over 1 year ago in Cloud Pak for Data / Watson Machine Learning Accelerator 0 Future consideration

Additional Implementations of Hyperparameter Optimization Algorithms

The current methods for hyperparameter optimization available in WMLA are limited. While the new BYOF framework allows end users to wrap implementations of additional methods, it would be desirable to have a few more ones supported by default:- A...
over 1 year ago in Cloud Pak for Data / Watson Machine Learning Accelerator 0 Future consideration

Make Bayesian Optimization parallelize more

Currently the implementation of Bayesian Optimization in WMLA is completely sequential. This renders it essentially useless for models with long training times. It would be ideal to parallelize it more. There are two angles to this: first, at it...
over 1 year ago in Cloud Pak for Data / Watson Machine Learning Accelerator 0 Future consideration

Include all metrics (not just the one being optimized) in the output of GET hypersearch

currently when one queries the results of a hyperparameter optimization round in Watson Machine Learning Accelerator, e.g. withhttps://<>:9243/platform/rest/deeplearning/v1/hypersearch/username-hpo-860740667802465... only the metric being op...
over 1 year ago in Cloud Pak for Data / Watson Machine Learning Accelerator 0 Future consideration