Abstract: In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.

Abstract: A device may receive software data identifying current logs and events associated with software products utilized by an entity and may process the software data, with a machine learning model, to generate error severity scores for the software products. The machine learning model may be trained based on historical software data identifying events and logs associated with software products utilized by the entity and based on a combination of historical health scores, historical sentiment scores, and historical dissimilarity scores for the software products. The device may process the error severity scores, with a prioritization model, to generate prioritized error scores and may process the error severity scores and the prioritized error scores, with a root cause analysis model, to generate root cause data identifying root causes associated with the error severity scores. The device may perform one or more actions based on the root cause data.

Abstract: In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.

Abstract: A device may receive software data identifying current logs and events associated with software products utilized by an entity and may process the software data, with a machine learning model, to generate error severity scores for the software products. The machine learning model may be trained based on historical software data identifying events and logs associated with software products utilized by the entity and based on a combination of historical health scores, historical sentiment scores, and historical dissimilarity scores for the software products. The device may process the error severity scores, with a prioritization model, to generate prioritized error scores and may process the error severity scores and the prioritized error scores, with a root cause analysis model, to generate root cause data identifying root causes associated with the error severity scores. The device may perform one or more actions based on the root cause data.

Abstract: A device may receive, from the client device, provisioning data selected from a user interface and identifying a cloud provider or on premise resources, an infrastructure, applications, tools, and artificial intelligence (AI) assets for provisioning a customized platform. The device may process the provisioning data, with a machine learning model, to identify conflicting or redundant applications, tools, or AI assets and to generate updated provisioning data, and may determine computing resource data identifying computing resources required for execution of the applications, tools, and AI assets. The device may obtain the applications, tools, and AI assets from a data structure, and may generate a customized template based on the computing resources and the applications, tools, and AI assets. The device may execute the customized template to provision the computing resources with the applications, tools, and AI assets and to create the customized platform.

Abstract: A device may receive, from the client device, provisioning data selected from a user interface and identifying a cloud provider or on premise resources, an infrastructure, applications, tools, and artificial intelligence (AI) assets for provisioning a customized platform. The device may process the provisioning data, with a machine learning model, to identify conflicting or redundant applications, tools, or AI assets and to generate updated provisioning data, and may determine computing resource data identifying computing resources required for execution of the applications, tools, and AI assets. The device may obtain the applications, tools, and AI assets from a data structure, and may generate a customized template based on the computing resources and the applications, tools, and AI assets. The device may execute the customized template to provision the computing resources with the applications, tools, and AI assets and to create the customized platform.

Abstract: A device may receive data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.

Abstract: A device may receive data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.