Mayur Aggarwal

Detail-oriented Cloud Architect designs, develops and maintains highly scalable, secure and reliable data structures. Accustomed to working closely with software architects and design analysts to understand business or industry requirements to develop comprehensive data applications. Proficient at developing database architectural strategies at the modeling, design and implementation stages.

Won R&D Award at NCR.

• Date of Birth: 09/25/92
• Gender: Male
• Marital Status: Single / Unmarried

Predictive Analytics for Cyber

It is a platform that leverages Machine Learning techniques to extract insights from SIEM tools such as Google Chronicle, enabling the detection and prediction of various types of cyber threats. 

My responsibilities in this role include streamlining and enhancing the solution to be more cloud-native and serverless, building and deploying ML pipelines, integrating data and new models, monitoring model performance, and designing data models in BigQuery. 

I have utilized various tools to accomplish these tasks, including GKE AutoPilot, BigQuery, Cloud Functions, Google Chronicle, KubeFlow, Vertex Pipelines, Looker, Azure ML, and Azure DevOps. 

AICOE - Data Platform

The AICOE - Data Platform is an important initiative aimed at consolidating all AI/ML-related data in one centralized hub. This platform significantly improved collaboration and knowledge sharing among teams. 

My responsibilities in this role include overseeing the integration of the Unity Catalog to establish a robust data governance layer within the AI Center of Excellence. Successfully designed and deployed Azure Databricks, ensuring optimal performance and scalability. 

Leveraged key tools such as Vertex AI, Databricks, Unity Catalog, and ADLS (Azure Data Lake Storage) to streamline and enhance the effectiveness of AI/ML activities within the organization. 

Self-Healing Event Rule Engine 

SeeR is an intelligent platform that looks through stress signals from devices in the field and translates those into incidents for the Service Desk to fix. The self-healing platform tracks the signals from the different NCR devices and takes pre-defined actions to self-correct error conditions that could end up otherwise in service outages to our customers. 

Lead the design and implementation of the Rule Engine. It allows NCR to execute real-time business rules for incidents and dispatch management systems as well as device events. 

Tools Used: Event Hub, Streaming Analytics, Logic Apps, Function Apps, Log Analytics, Azure Dashboards, and Table Storage. 

• Date of Birth: 09/25/92
• Gender: Male
• Marital Status: Single / Unmarried

Predictive Analytics for Cyber

It is a platform that leverages Machine Learning techniques to extract insights from SIEM tools such as Google Chronicle, enabling the detection and prediction of various types of cyber threats. 

My responsibilities in this role include streamlining and enhancing the solution to be more cloud-native and serverless, building and deploying ML pipelines, integrating data and new models, monitoring model performance, and designing data models in BigQuery. 

I have utilized various tools to accomplish these tasks, including GKE AutoPilot, BigQuery, Cloud Functions, Google Chronicle, KubeFlow, Vertex Pipelines, Looker, Azure ML, and Azure DevOps. 

AICOE - Data Platform

The AICOE - Data Platform is an important initiative aimed at consolidating all AI/ML-related data in one centralized hub. This platform significantly improved collaboration and knowledge sharing among teams. 

My responsibilities in this role include overseeing the integration of the Unity Catalog to establish a robust data governance layer within the AI Center of Excellence. Successfully designed and deployed Azure Databricks, ensuring optimal performance and scalability. 

Leveraged key tools such as Vertex AI, Databricks, Unity Catalog, and ADLS (Azure Data Lake Storage) to streamline and enhance the effectiveness of AI/ML activities within the organization. 

Self-Healing Event Rule Engine 

SeeR is an intelligent platform that looks through stress signals from devices in the field and translates those into incidents for the Service Desk to fix. The self-healing platform tracks the signals from the different NCR devices and takes pre-defined actions to self-correct error conditions that could end up otherwise in service outages to our customers. 

Lead the design and implementation of the Rule Engine. It allows NCR to execute real-time business rules for incidents and dispatch management systems as well as device events. 

Tools Used: Event Hub, Streaming Analytics, Logic Apps, Function Apps, Log Analytics, Azure Dashboards, and Table Storage.