A passionate Data Scientist. I love working with the entire data lifecycle—from exploring raw data to uncover hidden insights, to building intelligent models and deploying them as scalable, real-world applications on the cloud.
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📊 I’m currently analyzing: F1 Prediction Dataset, performing exploratory data analysis (EDA) with Seaborn and Matplotlib to identify key trends and prepare the data for model building.
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🔭 I’m building: A CI/CD pipeline using GitHub Actions to automate the testing and containerization of a PyTorch image classification model. The final artifact is a versioned Docker image pushed to AWS Elastic Container Registry (ECR).
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🌱 I’m learning: How to effectively use AWS SageMaker for distributed training and hyperparameter tuning, and exploring Infrastructure as Code (IaC) with Terraform to manage my cloud resources reproducibly.
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👯 I’m looking to collaborate on: Open-source projects that involve model performance monitoring, creating automated retraining pipelines, or deploying ML models as scalable APIs using tools like FastAPI or Flask.
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💬 Ask me about: Data analysis with Pandas & NumPy, creating visualizations with Matplotlib & Seaborn, and the end-to-end MLOps lifecycle from model to production.
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⚡ Fun fact: My models don't just have high accuracy, they have low latency too! 😉
Data Analysis & Visualization:
ML & Frameworks:
Cloud & MLOps:
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