Real-Time Scalable Customer Segmentation using Kubernetes
![](images/kuber_doc_seg.png)
Developed a scalable model with real-time inference capabilities using FastAPI, Docker, and Kubernetes to segment customers for tailored marketing strategies.
Hello 👋, I'm Abhishek!
A Master’s graduate in Data Science, I specialize in developing and managing end-to-end machine learning life cycles, MLOps, and creating AI applications using LLMs. My portfolio above showcases innovative projects that demonstrate my expertise. For more about me, my skills, or specific projects, feel free to interact with the chatbot here. It’s ready to provide answers and insights into my professional journey.
Developed a scalable model with real-time inference capabilities using FastAPI, Docker, and Kubernetes to segment customers for tailored marketing strategies.
Developed a robust and scalable solution to predict employee churn, leveraging Azure Databricks for real-time inference with auto-scaling REST API endpoints.
Developed a predictive maintenance application utilizing modular programming, Docker, AWS, and CI/CD pipelines for seamless deployment and scalability.
Developed a robust system to predict loan defaults using advanced machine learning techniques and deployed it on Microsoft Azure for scalable, real-time predictions.
Developed an AI tool using Google's Gemini LLM, SQL, and Plotly Express, enabling users to easily query data in natural language and receive insightful visualizations instantly.
Created an AI assistant using LangChain, OpenAI LLM, and Streamlit to fetch answers from multiple URLs and summarize PDFs for efficient information consumption.
Crafted a Tableau dashboard for visualizing sales metrics, regional trends, and market insights, enabling strategic decision-making through detailed profit and pricing analysis.
Developed a Power BI dashboard, integrating SQL for deep analysis of login frequencies and participation rates, enhancing digital education by optimizing engagement strategies.