Accenture Global Solutions Limited

Santhosh Mv

Koushik M Vijayaraghavan

Aditi Kulkarni

Rajendra Prasad Tanniru

Vijeth Srinivas Hegde

Ravindra Kabbinale

Amrutha Pervody Bhat

Sreenath Kothavoor

Dilip Krishnan

Sneha S Shekar

Sumanth Channegowda

Bhavana V Gudi

Sherin Varghese

Lakshmi Srinivasan

Shruthi Dhivakaran

Ravi Kiran Singh

Vinay Chamarthi

Meghana B Srinath

Naveen Raj K P

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.

Serverless environment-based provisioning and deployment system

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.

Utilizing automatic labelling, prioritizing, and root cause analysis machine learning models and dependency graphs to determine recommendations for software products

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Kasaragod, India

Santhosh Mv