[jira] [Updated] (IGNITE-11072) [ML] Prepare an example of model inference in SQL
[ https://issues.apache.org/jira/browse/IGNITE-11072?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yury Babak updated IGNITE-11072: Ignite Flags: (was: Docs Required) > [ML] Prepare an example of model inference in SQL > - > > Key: IGNITE-11072 > URL: https://issues.apache.org/jira/browse/IGNITE-11072 > Project: Ignite > Issue Type: Improvement > Components: ml >Affects Versions: 2.8 >Reporter: Anton Dmitriev >Assignee: Anton Dmitriev >Priority: Major > Fix For: 2.8 > > Time Spent: 10m > Remaining Estimate: 0h > > Machine learning model lifecycle assumes training followed by inference. The > inference is relatively simple procedure that is essentially a predefined > function call. The predefined function, or model in other words, can be > chosen from internal storage and be called directly from SQL. It will > significantly simplify usage of existing models. > > The goal of this task is to prepare an example that demonstrates how to do it > using existing functionality (With model storage prepared in IGNITE-10287 and > Liner Regression prepared in IGNITE-7438). -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-11072) [ML] Prepare an example of model inference in SQL
[ https://issues.apache.org/jira/browse/IGNITE-11072?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-11072: Description: Machine learning model lifecycle assumes training followed by inference. The inference is relatively simple procedure that is essentially a predefined function call. The predefined function, or model in other words, can be chosen from internal storage and be called directly from SQL. It will significantly simplify usage of existing models. The goal of this task is to prepare an example that demonstrates how to do it using existing functionality (With model storage prepared in IGNITE-10287 and Liner Regression prepared in IGNITE-7438). was: Machine learning model lifecycle assumes training followed by inference. The inference is relatively simple procedure that is essentially a predefined function call. The predefined function, or model in other words, can be chosen from internal storage and be called directly from SQL. It will significantly simplify usage of existing models. The goal of this task is to prepare an example that demonstrates how to do it using existing functionality. > [ML] Prepare an example of model inference in SQL > - > > Key: IGNITE-11072 > URL: https://issues.apache.org/jira/browse/IGNITE-11072 > Project: Ignite > Issue Type: Improvement > Components: ml >Affects Versions: 2.8 >Reporter: Anton Dmitriev >Assignee: Anton Dmitriev >Priority: Major > Fix For: 2.8 > > > Machine learning model lifecycle assumes training followed by inference. The > inference is relatively simple procedure that is essentially a predefined > function call. The predefined function, or model in other words, can be > chosen from internal storage and be called directly from SQL. It will > significantly simplify usage of existing models. > > The goal of this task is to prepare an example that demonstrates how to do it > using existing functionality (With model storage prepared in IGNITE-10287 and > Liner Regression prepared in IGNITE-7438). -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-11072) [ML] Prepare an example of model inference in SQL
[ https://issues.apache.org/jira/browse/IGNITE-11072?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-11072: Description: Machine learning model lifecycle assumes training followed by inference. The inference is relatively simple procedure that is essentially a predefined function call. The predefined function, or model in other words, can be chosen from internal storage and be called directly from SQL. It will significantly simplify usage of existing models. The goal of this task is to prepare an example that demonstrates how to do it using existing functionality. was:Machine learning model lifecycle assumes training followed by inference. The inference is relatively simple procedure that is essentially a predefined function call. The predefined function, or model in other words, can be chosen from internal storage and be called directly from SQL. It will significantly simplify usage of existing models. > [ML] Prepare an example of model inference in SQL > - > > Key: IGNITE-11072 > URL: https://issues.apache.org/jira/browse/IGNITE-11072 > Project: Ignite > Issue Type: Improvement > Components: ml >Affects Versions: 2.8 >Reporter: Anton Dmitriev >Assignee: Anton Dmitriev >Priority: Major > Fix For: 2.8 > > > Machine learning model lifecycle assumes training followed by inference. The > inference is relatively simple procedure that is essentially a predefined > function call. The predefined function, or model in other words, can be > chosen from internal storage and be called directly from SQL. It will > significantly simplify usage of existing models. > > The goal of this task is to prepare an example that demonstrates how to do it > using existing functionality. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
