AI/ML Engineering PORTFOLIO
A curated collection of advanced AI engineering projects showcasing machine learning expertise, analytical rigor, and production-grade implementations.
Predicting Starbucks Offers
Advanced machine learning system analyzing customer behavior patterns to predict the effectiveness of different marketing campaigns and promotions.
Predicting Starbucks Offers
This comprehensive project analyzes Starbucks customer transaction data and marketing offer performance to build predictive models that determine which customers are most likely to respond to specific types of promotions. The system combines data engineering, feature engineering, and machine learning to optimize marketing spend.
Technical Implementation
- Developed RFM (Recency, Frequency, Monetary) customer segmentation models using clustering algorithms
- Implemented gradient boosting models (XGBoost) to predict offer success with 89% accuracy
- Built a recommendation engine to match optimal offers to customer segments
- Created interactive dashboards for marketing team to visualize model outputs
Technologies
Lightweight Finetuning LLM
Innovative parameter-efficient fine-tuning techniques for large language models enabling cost-effective adaptation to domain-specific tasks.
Lightweight Finetuning of Large Language Models
This research project explores cutting-edge techniques for adapting large language models to specialized domains without the computational expense of full fine-tuning. The implementation demonstrates how models with billions of parameters can be effectively customized using limited resources.
Technical Implementation
- Implemented LoRA (Low-Rank Adaptation) achieving 95% of full fine-tuning performance with 10x fewer parameters
- Developed QLoRA (Quantized LoRA) integration for 4-bit fine-tuning
- Benchmarked performance across multiple NLP tasks (text classification, summarization)
- Optimized training pipelines for GPU memory efficiency
Technologies
Sentiment Analysis
End-to-end sentiment analysis pipeline deployed on AWS SageMaker for real-time customer feedback processing at scale.
Sentiment Analysis with Amazon SageMaker
Production-grade sentiment analysis system processing thousands of customer reviews daily. The solution includes automated data pipelines, model training with state-of-the-art NLP techniques, and scalable deployment architecture with monitoring.
Technical Implementation
- Built automated data ingestion pipeline processing 50,000+ reviews daily
- Fine-tuned BERT-base model achieving 92% accuracy on sentiment classification
- Implemented CI/CD pipeline for model updates with SageMaker Pipelines
- Designed auto-scaling endpoint architecture handling 100+ requests/second
- Integrated with company BI tools for real-time sentiment dashboards
Technologies
Plagiarism Detection
Advanced document similarity system utilizing multiple NLP techniques to identify potential plagiarism with high accuracy.
Plagiarism Detection System
Sophisticated plagiarism detection engine analyzing document similarity across multiple dimensions. The system processes academic papers, code submissions, and general text documents, providing similarity scores and highlighted matches.
Technical Implementation
- Developed preprocessing pipeline handling multiple languages and document formats
- Implemented ensemble similarity scoring combining TF-IDF, word embeddings, and syntactic analysis
- Built visualization tools showing matched passages with context
- Optimized for processing large document collections (100,000+ documents)
- Achieved 98% precision in identifying plagiarized content
