• 🎓 MSc Data Science University of Europe for Applied Sciences, Potsdam, Germany — since 09/2025
• 💼 Associate Cloud Engineer @ ArisGlobal, Mysuru, India — 10/2021 – 09/2025
• 📍 Berlin (Teltow), Germany

I'm a Data Science master's student with 4 years of professional experience as a Cloud Engineer, now focused on building data-driven applications that combine machine learning, cloud computing, and analytics to solve real-world problems.

• 🤖 Machine Learning
• ☁️ Cloud Computing (AWS)
• 📊 Data Analytics & Visualization
• 🌸 Large Language Models (LLMs)
• 🚀 AI-powered Applications

Associate Cloud Engineer — ArisGlobal, Mysuru, India 10/2021 – 09/2025 Worked on AWS cloud infrastructure and implementation consulting for enterprise deployments, building a strong analytical foundation in SQL, Python, Excel, and Power BI along the way.

Languages & Data

ML & Analytics

Visualization & BI

A data-visualization study of global maritime trade (2019–2026) using IMF PortWatch's daily, satellite-derived AIS data for ~90,000 vessels. Answers 10 analytical questions on how the 2023–24 Red Sea and Panama Canal disruptions rerouted trade rather than shrinking it, via a shared pipeline feeding both a notebook and a live dashboard. Tools: Plotly · Streamlit · IMF PortWatch / ArcGIS API · World Bank LPI API · NY Fed GSCPI Languages: Jupyter Notebook, Python, HTML Live: Streamlit app

A hybrid caching layer for LLM applications that checks an exact-match cache, then a semantic cache (sentence-embedding similarity via FAISS) before falling back to a local LLM — cutting latency, cost, and repeated inference. Includes a benchmarking and threshold-tuning framework for cache-hit quality. Tools: FAISS (vector search) · Sentence-Transformers (all-MiniLM-L6-v2) · Ollama (local LLM) Languages: Python

A machine-learning system that recommends the best-suited crop for a plot of land from its soil nutrients (N/P/K) and climate (temperature, humidity, pH, rainfall), trained on the Kaggle Crop Recommendation dataset (2,200 records, 22 crop classes). Ships as a Streamlit app with explainable feature importances plus a Power BI dashboard for exploring the data. Tools: scikit-learn (Random Forest, 99.32% test accuracy) · Streamlit · Power BI Languages: Jupyter Notebook, Python

An end-to-end ML pipeline for weakly-labelled bird-sound classification, built around the Kaggle BirdCLEF 2026 dataset. Converts field-recorded audio into Log-Mel spectrograms and classifies them with a 2D CNN using Multiple-Instance-Learning pooling over frame-level predictions, since only file-level species tags are available. A Streamlit dashboard lets you upload audio and view spectrograms, species predictions, and confidence scores. Tools: CNN / deep learning (Multiple-Instance-Learning pooling) · Streamlit Languages: Python, Jupyter Notebook

A full-stack decision-support system for retail inventory management: a normalized multi-store, multi-supplier PostgreSQL schema with live stock-health monitoring, reorder alerts, and days-to-expiry tracking. Derived-field logic (stock status, reorder alerts, capacity) is implemented once on the backend and imported directly by the frontend so the UI can never disagree with the API. Tools: Next.js (frontend) · Express REST API · PostgreSQL · Joi validation Languages: JavaScript, PLpgSQL, CSS

• 📧 Email: yashubopannapd033@gmail.com
• 💼 LinkedIn: linkedin.com/in/yashu-bopanna
• 🌐 Portfolio: bopanna012.github.io