Machine Learning Team Project: SVM, Decision Tree, KNN

New Blog Post: An introduction to MemPalace: a local, open-source agent memory system that organises knowledge into a structured hierarchy rather than a flat vector index. Use it to learn how to give your AI agent a persistent memory — no cloud infrastructure, no API key, two dependencies. #python #LLMs #AI #agent

Turn PDFs, markdown, and raw text into lineage-aware knowledge graphs. Query with local, global, hybrid, naive, mix, and graph-aware retrieval. Run the full stack in Rust on PostgreSQL. Built for teams that want Graph-RAG quality without Python pipeline fragility, cloud lock-in, or opaque retrieval behavior. https://edgequake.com/ #KnowledgeGraphs #GraphRAG

✅ Day 141/365 — Streams, Sorted Lists & Enterprise Features No days off. Here's what went down today: Cloud — AWS Kinesis Data Streams Dived deep into Kinesis Data Streams — one of the core building blocks for real-time data pipelines on AWS. Went beyond just the concept; spun up an actual stream, produced data into it, and consumed it on the other end. Seeing the data flow in real time hits different. LeetCode — Merge Two Sorted Lists Solved it. 0ms runtime — beating 100% of all Python submissions. The approach: collect both lists, sort, rebuild the linked list. Clean and effective. Building — Product is getting serious Today's shipped features: → Role-based access (owner / editor / viewer) → Invite system with real, working links → Code version history (think Git, but lite) → Product is starting to feel genuinely enterprise-level Every day, the gap between where I started and where I'm going gets clearer. 141 days in — no looking back. #365DaysOfCode #AWS #Kinesis #CloudComputing #LeetCode #Python #BuildInPublic #SoftwareEngineering #Day141

Latest Project - Fully deployed Sentiment Analysis API built on Azure.   The stack: • Azure Functions (Python 3.11) — serverless compute, no idle cost • Azure AI Language — pretrained sentiment model, no ML training required • Azure API Management — gateway handling routing and policy enforcement • Terraform — entire infrastructure provisioned as code, reproducible and version controlled • Azure Cost Management — budget alerts configured from day one   The API accepts customer review text and returns sentiment (positive/neutral/negative), confidence scores, and extracted key phrases — the kind of data a business can use to track product popularity and customer opinion at scale.   This was a learning project with a real goal: not just getting it to deploy, but being able to explain and defend every architectural decision. I debugged APIM policy tier incompatibilities, route path mismatches, and the distinction between infrastructure provisioning and code deployment — all things that don't show up in tutorials but absolutely show up in production.   Full code and README on GitHub: https://lnkd.in/gvmPDyj7   #Azure #CloudEngineering #Terraform #Serverless #Python #AzureFunctions #DevOps #CloudArchitecture #PortfolioProject

My multi-agent cross cloud article on deploying a Live multi-modal agent to Amazon Lightsail was accepted on the Generative AI channel on Medium!! This paper covers a complete solution that uses the ADK, Python Backend, Websockets, and a Live real-time Gemini Model. The entire system was deployed to an AWS Lightsail endpoint. The Generatative AI channel on Medium is here: https://generativeai.pub/ My article/demo is here: https://lnkd.in/eVBxUGfk #GDE #ADK #A2A #Python #GooogleCloud #AWScommunity #AWS #AWSCommunityBuilders #Lightsail

🚀 Just Built an Enterprise RAG Q&A System using AWS Bedrock! I recently developed an Enterprise-grade Retrieval-Augmented Generation (RAG) system that can answer questions based on custom documents using AWS Bedrock Knowledge Bases. 💡 This project demonstrates how Generative AI can be combined with real-world cloud infrastructure to build scalable and intelligent applications. 🔧 What I built: End-to-end RAG pipeline using AWS Bedrock Semantic search powered by vector embeddings Document ingestion via Amazon S3 Vector storage using OpenSearch Serverless Secure architecture using IAM roles (no hardcoded credentials) 🧠 How it works: User Query → Knowledge Base → Vector Search → LLM → Final Answer ⚙️ Tech Stack: Python | AWS Bedrock | Boto3 | Amazon S3 | OpenSearch Serverless ⚡ Challenges I faced: AWS service activation delays IAM role & permission issues Model availability across regions OpenSearch setup complexity ✅ What I learned: Importance of IAM roles in secure cloud apps Real-world implementation of RAG pipelines Debugging AWS service-level issues Designing scalable AI systems 📌 This project is a step towards building production-ready GenAI applications. 🔗 GitHub Repo: https://lnkd.in/ge5Z4GRm #AWS #GenerativeAI #RAG #MachineLearning #DataScience #CloudComputing #AWSBedrock #Python #OpenSearch #AIProjects

I was tired of manually reviewing Terraform PRs… so I built an AI reviewer 🤖 Now every Pull Request: ✔️ Gets a risk score ✔️ Flags security issues ✔️ Suggests fixes inline ✔️ Runs automatically via GitHub Actions Powered by Azure OpenAI + Python. The idea was simple: 👉 Catch risky infrastructure changes BEFORE deployment 👉 Reduce manual effort in code reviews This turned out to be a great hands-on way to explore: DevSecOps AI in CI/CD Terraform best practices Still improving it, but excited about where this can go. Curious — would you trust AI to review your infra code? 🤔 #DevOps #AI #Terraform #Cloud #Azure #GitHub https://lnkd.in/gf6-6JbV

I'm happy to announce that my talk 𝗣𝘆𝘁𝗵𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 has been accepted in Azure Cloud Lab 🚀 This is the talk description: GenAI projects often start as exciting experiments — and quickly turn into unmaintainable chaos. How do we move from notebooks and proofs of concept to clean, scalable, production-grade systems? In this talk, we'll present a practical Python architecture for Generative AI projects, designed to scale across teams and environments. We'll cover tools, structural patterns and cloud integration with Azure services. Because GenAI isn't just about prompts — it's about engineering.

The goal of an Agentic runtime is to give you a platform to build production-grade Agents. In the world of Amazon Web Services (AWS), that's AgentCore. You can programmatically build your Agent in Python to connect to MCP Server tools, route through agentgateway, and perform a particular action on your behalf. #agenticai #ai

Building Model Context Protocol (MCP) servers from scratch is a waste of your time. Connecting LLMs to your private data shouldn't take weeks of engineering. Yet most developers get completely bogged down in boilerplate code before they even think about deployment. There is a much faster way to ship. In today's daily audio pill, we break down a streamlined workflow to get your Python MCP servers live in record time. Here is the exact stack we cover: • Python for the core logic • Gemini CLI to instantly generate boilerplate • AWS ECS Express for rapid cloud deployment This combination drastically reduces the friction of giving your AI models secure context. Stop fighting with infrastructure and start leveraging your data. Listen to the short episode or read the full script to steal this deployment strategy. You can find the link to the full newsletter in the comments below. 🎙️ #AI #Python #CloudComputing