Attempting to train Predictive Intelligence Solutions results in the solution perpetually sitting at the "Waiting for Training" statusIssue <!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: #000000; } span { font-size: 12pt; font-family: Lato; color: #000000; } h2 { font-size: 24pt; font-family: Lato; color: black; } h3 { font-size: 18pt; font-family: Lato; color: black; } h4 { font-size: 14pt; font-family: Lato; color: black; } a { font-size: 12pt; font-family: Lato; color: #00718F; } a:hover { font-size: 12pt; color: #024F69; } a:target { font-size: 12pt; color: #032D42; } a:visited { font-size: 12pt; color: #00718f; } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } It may be discovered that an NLU Machine Learning system Predictive Intelligence Solution will perpetually remain at the "Waiting for Training" State, even up to several days and never show any training progress. Eventually the training process may hit the time-out threshold and change to a status of "Training request timed out". Nevertheless the Solution Definition itself will never actually be trained and thus a usable solution on the instance. Normally, immediately after the solution is submitted for training, the solution is expected to thus remain at this stage while waiting for the NLU scheduler to detect and queue this solution for training and solutions will be processed in the order that they are submitted and thus the solution is expected to remain in the "Waiting for Training" stage for some amount of time. However, this should be for only a relatively short time, and thus, if the solution definition never advances past this phase, this usually indicates an issue. Symptoms<!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: #000000; } span { font-size: 12pt; font-family: Lato; color: #000000; } h2 { font-size: 24pt; font-family: Lato; color: black; } h3 { font-size: 18pt; font-family: Lato; color: black; } h4 { font-size: 14pt; font-family: Lato; color: black; } a { font-size: 12pt; font-family: Lato; color: #00718F; } a:hover { font-size: 12pt; color: #024F69; } a:target { font-size: 12pt; color: #032D42; } a:visited { font-size: 12pt; color: #00718f; } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } As mentioned, the solution will remain at the "Waiting for Training" status for an extended amount of time, and eventually will, once reaching a system configured time-out quota value, will change to a "Training request timed out" status. This will happen for any solution on the instance and thus these solutions will never complete the training and this current version of the solutions cannot be used within the Machine Learning system on the instance. Release<!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: #000000; } span { font-size: 12pt; font-family: Lato; color: #000000; } h2 { font-size: 24pt; font-family: Lato; color: black; } h3 { font-size: 18pt; font-family: Lato; color: black; } h4 { font-size: 14pt; font-family: Lato; color: black; } a { font-size: 12pt; font-family: Lato; color: #00718F; } a:hover { font-size: 12pt; color: #024F69; } a:target { font-size: 12pt; color: #032D42; } a:visited { font-size: 12pt; color: #00718f; } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } All current release versions. Cause<!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: #000000; } span { font-size: 12pt; font-family: Lato; color: #000000; } h2 { font-size: 24pt; font-family: Lato; color: black; } h3 { font-size: 18pt; font-family: Lato; color: black; } h4 { font-size: 14pt; font-family: Lato; color: black; } a { font-size: 12pt; font-family: Lato; color: #00718F; } a:hover { font-size: 12pt; color: #024F69; } a:target { font-size: 12pt; color: #032D42; } a:visited { font-size: 12pt; color: #00718f; } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } There are a few potential causes that a solution may repeatedly remain at the "Waiting for Training", however one of the most common such reasons discovered recently, in particular if the NLU system itself is properly configured, is due to the account as used by the NLU System "sharedservice.worker" does not have the proper permissions needed to access these solutions and the necessary system components. At a minimum, the sharedservice.worker should currently be set as an active account, and should contain the following roles: platform_ml_readplatform_ml_createplatform_ml_write In addition, since this particular Service account on the instance is often used by other internal instance processing, it may also have and need other roles associated to it as well. In addition, if the instance has the Explicit Roles plugin installed on it, the sharedservice.worker User account [sys_user] should also include the snc_internal role. If these roles are not found to be associated to this User Account, this is most probable the reason the NLU system is unable to thus train these solutions. Resolution<!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: #000000; } span { font-size: 12pt; font-family: Lato; color: #000000; } h2 { font-size: 24pt; font-family: Lato; color: black; } h3 { font-size: 18pt; font-family: Lato; color: black; } h4 { font-size: 14pt; font-family: Lato; color: black; } a { font-size: 12pt; font-family: Lato; color: #00718F; } a:hover { font-size: 12pt; color: #024F69; } a:target { font-size: 12pt; color: #032D42; } a:visited { font-size: 12pt; color: #00718f; } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } If the failure to be able to train the solutions is indeed due to the permissions and rights of the account as used by the NLU system for processing solutions, this can be fixed by adding these appropriate permissions to the necessary account. To thus correct the issue, browse to the User [sys_user] record for the account with the name sharedservice.worker. Ensure the account is set to a status of Active and is not set as "Locked out". If inactive, or locked out, update the account so that it shows as both Active and not "Locked out". Check the roles associated to the account and ensure it has the roles as mentioned above associated to the account (platform_ml_read, platform_ml_create, platform_ml_write). If this account is not found to have these roles, ensure to add each of these roles to the account. If the instance has the Explicit Roles plugin activated on the instance, ensure the account also has the snc_internal role associated to it.