Code With MEMO

Have a nice Sunday 🥰

EID MUBARAK HAPPY HOLIDAY FOR MUSLIM FAM

He's OpenAI CEO 😐

It seems nice. Prepared by our department head and IS HUB Lead

I'm tired computing by vote, Because it is completely unfair treat. basically I'm losing this computation🙌🙌

Basically this is for ISHUB Computation. so Wish me luck.

I have completed the ADWA AI Assistant. I had deployed it, but the new update (prompt) is not deployed because of a timeout issue on Render. To address this. There is also some lag issue. Here is the github repo: ADWA AI Here is the live link: ADWA AI @codewithmemo @codewithmemo

Django (Python) like A full-service restaurant with a professional kitchen How it works: The chef (server) prepares each dish from scratch when ordered Performance: Can handle many customers smoothly, but each order takes a moment to prepare Best for: Complex, data-heavy applications (Train models,..) PHP like A food truck that sets up fresh for every single customer How it works: Tears down the entire truck after each order, rebuilds it for the next Performance: Slower for complex tasks, simple for basic websites Best for: Simple websites, WordPress blogs JavaScript Frameworks (Node.js) like A 24/7 diner with servers who never sleep How it works: Always running, handles many orders simultaneously without waiting Performance: Blazing fast for real-time updates, can get messy with complex cooking Best for: Real-time apps, chat apps, streaming @codewithmemo @codewithmemo

do you know the difference between Django , PHP and Js Frameworks due to system performance? 👌

🚀 Top 25 Machine Learning Architecture Questions (Every ML Engineer Should Know) Machine Learning isn’t just about training models it’s about designing systems that scale, perform, and survive production. If you’re preparing for ML interviews, system design rounds, or real-world MLOps work, these are the most important ML Architecture questions you should be comfortable answering 🧠 Core ML Architecture Concepts 1️⃣ What is Machine Learning architecture and why does it matter? 2️⃣ Batch inference vs Real-time inference 3️⃣ What is model serving and common tools used 4️⃣ Data drift: what it is and how to handle it 5️⃣ Feature stores and their role in ML systems 6️⃣ What is MLOps and why it’s critical ⚙️ Training, Optimization & Pipelines 7️⃣ Training vs fine-tuning 8️⃣ Regularization techniques (L1, L2, Dropout, Early stopping) 9️⃣ Model versioning in production 🔟 ML pipelines and workflow automation 1️⃣1️⃣ CI/CD for ML systems 🗄 Data, Embeddings & Databases 1️⃣2️⃣ Choosing the right database for ML 1️⃣3️⃣ What are embeddings and why they’re powerful 1️⃣4️⃣ Handling sensitive data (GDPR, HIPAA, security) 📊 Monitoring, Explainability & Scaling 1️⃣5️⃣ Monitoring tools for ML models 1️⃣6️⃣ Explainability vs Interpretability 1️⃣7️⃣ Horizontal vs Vertical scaling 1️⃣8️⃣ Ensuring reproducibility in ML 1️⃣9️⃣ Factors affecting ML latency 🚢 Deployment & Production Strategies 2️⃣0️⃣ Why Docker/containerization matters 2️⃣1️⃣ GPU-accelerated deployment — when & why 2️⃣2️⃣ A/B testing in ML systems 2️⃣3️⃣ Multi-model deployment strategies 2️⃣4️⃣ Model rollback strategies 2️⃣5️⃣ Designing ML architectures for scalability