How I Built a Perfume Recommender with MLOps
Step-by-step breakdown of how I used TF-IDF, FastAPI, MLflow, and AWS to deploy a smart perfume recommendation system...
As a Data Scientist and AI Engineer, I specialize in building intelligent systems using Python, Machine Learning, Deep Learning, MLOps, and visualization tools. I've worked on end-to-end MLOps pipelines, deployed scalable models, and developed interactive dashboards that transform raw data into actionable insights.
About MeExpertise in Python, SQL, and data analysis with tools like Pandas and Tableau, delivering actionable insights.
Experienced in Pandas, Excel, and Streamlit. Built dashboards for Olympic trends and medical analytics using real-world datasets.
Experience in sentiment analysis and chatbots, reducing response time by 25% in medical apps.
Chatbot for disease prediction, mental health response, and medical Q&A using sentence transformers.
Streamlit dashboard visualizing athlete stats, medal trends, and gender participation using Seaborn & Plotly.
TF-IDF and cosine similarity based recommender system with 95% accuracy. Full MLOps pipeline included.
Deployed ML models with FastAPI, Docker, Kubernetes, MLflow, and monitored with Prometheus & Grafana.
Skin disease image classification, crop yield prediction, admin dashboard analytics, customer segmentation, patient health prediction and more...
SEE ALLStep-by-step breakdown of how I used TF-IDF, FastAPI, MLflow, and AWS to deploy a smart perfume recommendation system...
A guide on how to build context-aware Q&A chatbots for healthcare using NLP, embeddings, and Flask APIs...
Learn how I created an interactive Olympic data analysis dashboard with Seaborn, Streamlit, and Plotly...
Feel Free To Contact Me