Computer Science graduate from the University of Florida focused on building end-to-end machine learning systems and deploying them using MLOps and DevOps practices.

I work across the full lifecycle of ML systems — from data processing and modeling to scalable deployment and monitoring.

Currently exploring Generative AI, with a focus on large language models, fine-tuning techniques, and real-world applications of NLP systems.

Python • SQL
Pandas • NumPy • Scikit-learn • TensorFlow
Regression • Classification • Clustering
Neural Networks • Time Series (ARIMA, ETS, XGBoost)

Text Preprocessing • TF-IDF • Word Embeddings
Transformer-based Models (BERT)
LLM Fundamentals • Prompt Engineering • Fine-tuning

FastAPI • REST APIs • Streamlit
Docker • CI/CD (GitHub Actions)
MLflow • Batch & Real-time Inference
Monitoring & Logging

AWS (EC2 • S3 • SageMaker)
MySQL • MongoDB
Render

• Credit Risk Prediction System
End-to-end ML pipeline with real-time inference, feature engineering, and deployment using FastAPI and Docker.

• Large-Scale NLP System (14M+ Records)
Benchmarking classical ML and transformer-based models with a focus on scalability and performance.

• Time Series Forecasting Pipeline
Implemented ARIMA, ETS, and XGBoost models for forecasting with evaluation and comparison.

• ML Model Deployment System
Built scalable API-based inference systems with monitoring and optimized latency.

• ML Monitoring & Observability
Exploring system monitoring using Prometheus and Grafana for production ML systems.

Email: anishve9@gmail.com
LinkedIn: linkedin.com/in/anish-tv

⭐ Thanks for visiting my GitHub