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IBM Data and AI Ideas Portal for Customers


This portal is to open public enhancement requests against products and services offered by the IBM Data & AI organization. To view all of your ideas submitted to IBM, create and manage groups of Ideas, or create an idea explicitly set to be either visible by all (public) or visible only to you and IBM (private), use the IBM Unified Ideas Portal (https://ideas.ibm.com).


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:


Search existing ideas

Start by searching and reviewing ideas and requests to enhance a product or service. Take a look at ideas others have posted, and add a comment, vote, or subscribe to updates on them if they matter to you. If you can't find what you are looking for,


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,

  1. Post an idea

  2. Upvote ideas that matter most to you

  3. Get feedback from the IBM team to refine your idea


Specific links you will want to bookmark for future use

Welcome to the IBM Ideas Portal (https://www.ibm.com/ideas) - Use this site to find out additional information and details about the IBM Ideas process and statuses.

IBM Unified Ideas Portal (https://ideas.ibm.com) - Use this site to view all of your ideas, create new ideas for any IBM product, or search for ideas across all of IBM.

ideasibm@us.ibm.com - Use this email to suggest enhancements to the Ideas process or request help from IBM for submitting your Ideas.

IBM Data & AI Roadmaps (http://ibm.biz/Data-and-AI-Roadmaps) - Use this site to view roadmaps for Data & AI products.

IBM Employees should enter Ideas at https://hybridcloudunit-internal.ideas.aha.io/


Status Planned for future release
Created by Guest
Created on May 30, 2018

Publish WML performance statistics in the documentation

Latency and performance are key considerations when architecting systems to interact with machine learning models. Understanding the performance I can expect, and any limitations of services I am using is important as this information enables me to drive logical architecture decisions to meet my end goal.

WML has the potential to be a key component in many applications, however the lack of performance statistics/guarantees on delivery of results make it difficult to know how to incorporate the service in different contexts. e.g. average response times via REST, how many requests/second can the service handle before I should consider hosting the same model in a separate WML instance, and load balance between the two?

Please publish performance details and recommended sizing so that we can make informed decisions for architecting around WML.