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Evaluating Workflow Orchestration Frameworks NEW
Built hands-on POCs comparing Azure Durable Functions, Temporal, Prefect, and Dapr for orchestrating event-driven AI pipelines. Implemented the same invoice processing workflow (PDF → parse → fan-out/fan-in → aggregate) across all frameworks for an apples-to-apples comparison. Key insights on deterministic replay, multi-service architecture patterns, and how local dev experience impacts iteration speed and debugging.
CandleWise - Part 4: Automating Deployment CI/CD NEW
Automated my entire infrastructure using Terraform for Azure. Implemented a comprehensive CI/CD pipeline with GitHub Actions that handles my complete deployment strategy - automatically deploying the Next.js frontend to Vercel and the .NET Core backend to Azure App Service.
Zero-Cloud Image Clustering
This project uses lightweight models like MobileNet V3 and robust clustering algorithms (HDBSCAN and K-means) to create an efficient local image clstering solution. The implementation uses cosine similarity to compare high-dimensional image embeddings. Most importantly, it shows that modern edge devices can handle complex machine learning workflows while preserving user privacy and reducing dependency on external services.
Gesture Recognition Without the Cloud: Why On-Device AI Is a Game-Changer
Implement a privacy-preserving gesture recognition directly on your device using MediaPipe and OpenCV. Process gestures locally without cloud dependencies, ensuring user privacy while maintaining high performance. The solution combines powerful ML models with robust image processing for real-time analysis, all presented through an intuitive Streamlit interface.
Medical Calls Analysis in AWS (Part 1) - Getting Started
This guide focuses on setting up Amazon Bedrock, a serverless AI service by AWS. It covers account setup, AWS CLI configuration, accessing Bedrock, and generating a model response, including a hands-on example and troubleshooting tips.
Forget Chatbots! Ambient Agents Work While You Sleep!
MarketMind
Build a Restaurant Name Generator using LangChain and Streamlit
Build an application for generating unique restaurant names using Large Language Models (LLMs) and the LangChain framework. It covers setting up the environment ...
Medical Calls Analysis in AWS (Part 5) - Automating AWS Deployments with Terraform NEW
Learn how to automate AWS resource provisioning and management using Terraform. This guide covers version-controlled infrastructure, consistent deployments, and error-free configuration.
CandleWise - Part 2: Integrating a third party API
This article discusses the integration of real-time stock data into an application using IHttpClientFactory, a tool that manages HttpClient instances...
CandleWise - Part 3: Deploying App to Azure
The blog post details the process of deploying the app to Microsoft Azure. The process involves encapsulating the application within a Docker container, creating a Docker image of the application, and using Visual Studio tools for developing with Docker containers.
CandleWise - Part 1: Environment Setup
CandleWise, a portfolio management app that I'm building as part of my exploration journey into .NET Core development. This series documents my learning process with Visual Studio, Docker, and deployment to Azure. In this guide, we'll cover setting up a development environment by installing Visual Studio 2022 and .NET Core 6.0 SDK, then creating an ASP.NET Core Web API project as our foundation....
Medical Calls Analysis in AWS (Part 2) - Audio Transcription
Medical Calls Analysis in AWS (Part 3) - Smart Summarization with Amazon Bedrock
Medical Calls Analysis in AWS (Part 4) - Monitoring with AWS CloudWatch
Dragons vs Unicorns (Part I) - HTML and CSS
Dragons vs Unicorns (Part II) - JavaScript