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Report abuseI work at the intersection of Data Science, Generative AI, Cloud ML, and Data Engineering β building practical AI systems that move from idea to real-world impact.
| Area | What I Build |
|---|---|
| π§ Generative AI | RAG apps, agentic workflows, prompt engineering, LLM-powered automation |
| π MLOps | ML lifecycle, experiment tracking, deployment, model serving, monitoring-ready workflows |
| βοΈ Cloud AI | Azure ML, Azure OpenAI, AWS SageMaker, AWS Bedrock, scalable AI services |
| π Data Science | ML models, analytics, visualization, insight generation, decision support |
| πΈοΈ Graph + Connected Data | Graph thinking, relationship modelling, business context from connected data |
Local deployment of gpt-oss-20b model in AWS EC2 instance.
Python 8
This app leverages Semantic Caching to minimize inference latency and reduce API costs by reusing semantically similar prompt responses.
Python 9
An R package for visualizing data points from unknown distributions on a Pearson diagram, allowing users to evaluate and interpret skewness and kurtosis for effective statistical insights. It is usβ¦
It is an End-to-end MLOps pipeline for hotel reservation prediction leveraging GCP, LightGBM, MLFlow, Jenkins and Docker. This system automates data ingestion, preprocessing, feature selection, modβ¦
This is a collection of various document parsers and hands-on to construct structured data for your RAG applications.
Python 4
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