MRINAL BHARDWAJ

Data Driven OPS (DDO): Worked with TranAD multivariate deep learning model for Reconstruction of Time Series Data and Anomaly Detection. Used Prophet model for Time Series Forecasting and developed a Forecast Monitor. Developed LSTM Deep Learning model for Time Series Prediction. Utilized Auto Arima for memory optimization and clustering algorithms (K-Means, DBSCAN) for data analysis. Developed an Outlier Detection model using DBSCAN and Explainable AI (XAI). Leveraged GCP, Vertex AI Pipeline, and Docker for deployment. AIOPS: Developed end-to-end pipelines in Flask, integrated databases, and utilized Plotly and Streamlit for visualization. Conducted experiments with MLflow and Evidently AI to identify data drift.

Airport AI: Developed a Flask web application with UI components and MongoDB database models. Developed an LSTM Deep Learning model for Time Series Forecasting. Wearable AI: Conducted data analysis using Pandas, NumPy, and Scikit-Learn for heart rate anomaly detection. Created a health score calculation script and established connections with MongoDB cloud storage. Developed end-to-end pipelines using Flask.

Worked with several Machine Learning models, Data Science Implementations, and Python scripting. Developed Credit card Fraud Detection and Music Recommendation System projects.

University: A.P.J. Abdul Kalam Technical University