Watching Kindred evolve from an idea into something tangible has been an incredible journey of building, learning, feedback, and continuous improvement. I’m truly grateful to Decoding Data Science (DDS), the mentors, @Mohammad Ahmad and this amazing community for the guidance, encouragement, and opportunity to keep pushing ourselves. Wishing every fellow contestant the very best as we head into the final day—regardless of the outcome, everyone who dared to build, learn, and finish this journey is already a winner! #Kindred #DecodingDataScience #AgenticAI #AIInnovation #BuildInPublic #LearningByDoing

Day 7 — ProDiag AI | From Local Prototype to Production Architecture 🚀 Today was less about adding another UI feature—and more about making the system deployable, reliable, and production-oriented. I containerized the ProDiag AI V2 backend with Docker and validated the complete communication architecture: React Frontend → Flask Backend → MQTT/TLS → HiveMQ Cloud → Industrial Simulator The backend now includes the trained ML models, RAG components, knowledge base, and MQTT services inside a deployment-ready container. I also fixed an important initialization issue discovered during container testing: the database schema must be created before services such as the Alert Service attempt to load existing records. This is exactly the kind of issue that doesn't always appear during local development—but becomes critical when moving toward production. Building an AI system is not only about the model. The real challenge is making the entire system work together reliably. Mohammad Arshad Decoding Data Science #AI #IndustrialAI #PredictiveMaintenance #AgenticAI #MachineLearning #IIoT #Docker #MQTT #Engineering #ProDiagAI

I’m building ProofCheck — an AI Evidence Reconciliation Agent. The idea is simple: Don’t just summarize documents. Prove whether they agree. The project combines: • RAG and structure-aware document retrieval • Agentic investigation and tool use • Deterministic validation for calculations and evidence conflicts • Structured evidence and provenance • Safety boundaries and human-in-the-loop review • Evaluation with golden test scenarios One principle has guided the architecture: The LLM decides what to investigate. Deterministic systems establish what the numbers actually say. Today I completed the first working UI connected to the real backend, including investigation progress, error handling, and reset/clear behavior. There is still work to do — especially around reliability and edge cases — but that is exactly what makes this challenge valuable: building, testing, finding weaknesses, and improving the system one step at a time. On to the next checkpoint. 🚀 #AI #ArtificialIntelligence #AgenticAI #RAG #LLM #Python #AIEngineering #BuildInPublic #DecodingDataScience #AIChallenge

Today I worked on Figuro’s Practice feature and started building Weak Points. I focused on: 🎯 Different practice questions 🧠 Adaptive difficulty 📚 Lesson/material-based questions 💡 Hints and feedback 🔄 Try Again + Next Question 📊 Focusing practice on weak areas Still testing and improving, but Day 5 is done! 💻✨