Enterprise deployment framework with artificial intelligence/machine learning
A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.
1. A method, comprising:
managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities, wherein the managing step comprises predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks;
receiving a modification to the predicted time; and
inputting the modification as training data to a machine learning model executed by the machine learning module;
wherein the predicting step comprises inputting a multi-dimensional feature vector to the machine learning model executed by the machine learning module;
wherein features of the multi-dimensional feature vector comprise:
data identifying each of the multiple tasks;
data identifying a number of the multiple entities performing each of the multiple tasks; and
data identifying which of the multiple entities are performing each of the multiple tasks;
wherein the data identifying which of the multiple entities are performing each of the multiple tasks comprises data identifying one or more components performing version control for code management, gateway creation and message-oriented-middleware provider validation in connection with a release of the software program;
wherein the training data for the machine learning model further comprises the features of the multi-dimensional feature vector; and
wherein the steps of the method are executed by a processing device operatively coupled to a memory.
2. The method of claim 1 , wherein the managing step further comprises tracking a status of one or more of the multiple tasks.
3. The method of claim 2 , wherein the tracking is performed in real-time.
4. The method of claim 2 , wherein the managing step further comprises notifying one or more of the multiple entities regarding the status of one or more of the multiple tasks.
5. The method of claim 1 , wherein the managing step further comprises recommending one or more actions when an accuracy percentage of the predicting step falls below a given threshold level.
6. The method of claim 1 , wherein the multiple tasks are associated with one or more launch orchestration program processes.
7. The method of claim 1 , wherein the machine learning module executes a random forest algorithm.
8. An apparatus comprising:
a processor operatively coupled to a memory and configured to:
manage multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities, wherein in performing the managing, the processor is configured to predict a time taken for a given one of the multiple entities to complete a given one of the multiple tasks;
receive a modification to the predicted time; and
input the modification as training data to a machine learning model executed by the machine learning module:
wherein in performing the predicting, the processor is configured to input a multi-dimensional feature vector to the machine learning model executed by the machine learning module;
wherein features of the multi-dimensional feature vector comprise:
data identifying each of the multiple tasks;
data identifying a number of the multiple entities performing each of the multiple tasks; and
data identifying which of the multiple entities are performing each of the multiple tasks;
wherein the data identifying which of the multiple entities are performing each of the multiple tasks comprises data identifying one or more components performing version control for code management, gateway creation and message-oriented-middleware provider validation in connection with a release of the software program; and
wherein the training data for the machine learning model further comprises the features of the multi-dimensional feature vector.
9. The apparatus of claim 8 , wherein in performing the managing, the processor is further configured to track a status of one or more of the multiple tasks.
10. The apparatus of claim 9 , wherein the tracking is performed in real-time.
11. The apparatus of claim 9 , wherein in performing the managing, the processor is further configured to notify one or more of the multiple entities regarding the status of one or more of the multiple tasks.
12. The apparatus of claim 8 , wherein in performing the managing, the processor is further configured to recommend one or more actions when an accuracy percentage of the predicting step falls below a given threshold level.
13. The apparatus of claim 8 , wherein the multiple tasks are associated with one or more launch orchestration program processes.
14. The apparatus of claim 8 , wherein the machine learning module executes a random forest algorithm.
15. An article of manufacture comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device to perform the steps of:
managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities, wherein the managing step comprises predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks;
receiving a modification to the predicted time; and
inputting the modification as training data to a machine learning model executed by the machine learning module;
wherein the predicting step comprises inputting a multi-dimensional feature vector to the machine learning model executed by the machine learning module;
wherein features of the multi-dimensional feature vector comprise:
data identifying each of the multiple tasks;
data identifying a number of the multiple entities performing each of the multiple tasks; and
data identifying which of the multiple entities are performing each of the multiple tasks;
wherein the data identifying which of the multiple entities are performing each of the multiple tasks comprises data identifying one or more components performing version control for code management, gateway creation and message-oriented-middleware provider validation in connection with a release of the software program; and
wherein the training data for the machine learning model further comprises the features of the multi-dimensional feature vector.
16. The article of manufacture of claim 15 , wherein the managing step further comprises recommending one or more actions when an accuracy percentage of the predicting step falls below a given threshold level.
17. The article of manufacture of claim 15 , wherein the multiple tasks are associated with one or more launch orchestration program processes.
18. The article of manufacture of claim 15 , wherein the managing step further comprises tracking a status of one or more of the multiple tasks.
19. The article of manufacture of claim 18 , wherein the tracking is performed in real-time.
20. The article of manufacture of claim 18 , wherein the managing step further comprises notifying one or more of the multiple entities regarding the status of one or more of the multiple tasks.