Shravya Kuchulakanti

Data Collection and Monitoring: Installed and maintained sensors for continuous monitoring of huge data. Collected and logged data to build a comprehensive dataset for analysis. Statistical Analysis: Conducted descriptive statistics and correlation analyses to understand normal operating ranges and relationships between system parameters. Predictive Maintenance Using Machine Learning: Developed and trained machine learning models, including linear regression, logistic regression, support vector machines (SVM), and neural networks, to predict assets always communicate to user before any failures occur. Implemented anomaly detection algorithms to identify deviations from normal operating conditions. NLP and Computer Vision Integration: Utilised natural language processing (NLP) and computer vision techniques to analyse unstructured data and images, extracting insights for system optimisation and predictive maintenance. AI Tool Integration: Integrated generative AI models and Microsoft Azure AI services into predictive maintenance workflows, enhancing the accuracy and efficiency of diagnostics and maintenance scheduling. Leveraged Azure Machine Learning Studio for model development, deployment, and management. Real-Time Monitoring and Alerts: Integrated real-time data feeds into trained ML models for continuous system monitoring. Set up automated alerts for detected anomalies or predicted failures. System Optimization: Used insights from statistical and ML analyses, as well as AI tools, to optimize the performance of assets. Database Management: Employed KairosDB for time-series data storage and Cassandra for scalable and fault-tolerant data management. Utilized LoopBack tool for building and managing REST APIs. Deployment and Continuous Integration/Continuous Deployment (CI/CD): Utilized GIT for version control and collaborative development. Employed Docker for containerization of applications. Automated build and deployment processes using Jenkins and Azure DevOps. D

Developed Covid19 AI Chatbot using Rasa framework which uses NLU (Natural language understanding). Worked on Different ML Algorithms to predict concrete comprehensive strength. Worked on Web Scraping using Beautiful Soup library. Performed Exploratory Data Analysis using matplotlib and seaborn library.

Upskilled in Python, worked on ML. Got familiar with data visualisations and performed data Analysis on multiple projects. Assisted Data Scientists in developing algorithms for predictive modelling.

Worked on Google tools such as Chrome, Google Sheets, Docs, Slides, Drive. Experience on Managing and executing business operations. Worked on Youtube reviewing and take appropriate actions on the video based on the defined policies. Interaction with Customers to understand the policies and the impact of violations on end users.