Pavan Kumar Adapala

Topic: Modelling concepts for forecasting the energy consumption in industrial paint shops: An in-depth analytics study Department: MES/IIoT Smart Data Analytics - Developed Batch and One-step-Ahead forecasting models on IIoT data with 95% accuracy to predict industrial energy consumption - Evaluated models under four real-world scenarios and enhanced forecasting performance by 15% via advanced feature engineering - Delivered actionable insights to support sustainability and energy optimization

Research Internship: Design, development, and optimization of CI-CD-CT Jenkins pipelines for Parking systems - Designed and optimized Jenkins pipelines to streamline CI/CD workflows for advanced driver-assistance (ADAS) parking system software - Automated debugging tasks by integrating Python scripts with REST APIs, reducing issue resolution time by 30% - Built a centralized Grafana dashboard for monitoring CI/CD builds across multiple projects, enabling data-driven release planning and performance insights

- Provisioned and managed AWS infrastructure using Terraform and CloudFormation, supporting scalable and secure client environments - Developed custom CloudWatch dashboards and alerting systems to monitor application performance metrics and logs, enabling proactive incident response and minimizing downtime Technologies Used: AWS, Terraform, CloudFormation, CloudWatch, AWS CLI, Bash

- Contributed to a proof-of-concept cloud migration project, improving scalability and reducing infrastructure costs by 25% through AWS Auto Scaling - Implemented the CI/CD pipelines using Jenkins Master-Slave architecture to automate deployments on AWS, accelerating release cycles - Designed and deployed a scalable 3-tier AWS architecture to host a web application, ensuring high availability and fault tolerance - Collaborated with cross-functional teams to resolve infrastructure dependency issues