Top 20 Deep Learning Interview Questions & Answers (Part 1 of 2)
Author(s): Shahidullah Kawsar Originally published on Towards AI. Machine Learning Interview Preparation Part 10 Deep learning is a subset of machine learning that uses neural networks with multiple hidden layers to learn complex patterns directly from data. Deep learning models are trained …
Shipping Real Sales Forecasts: How Model Context Protocol Enables AI Agents to Use Your Data in Production
Author(s): Apoorvavenkata Originally published on Towards AI. If you have worked on sales forecasting as a product manager, data analyst, or data scientist, this situation will sound familiar. Most sales forecasts work well in notebooks but fall apart the moment they’re connected …
Building an AI Shopping Agent with UCP: From Concept to Production-Ready Code
Author(s): Jageen Shukla Originally published on Towards AI. Building an AI Shopping Agent with UCP: From Concept to Production-Ready Code Complete implementation guide: Ollama + Native Tool Calling + Production Patterns (with working repository) Ollama + Native Tool Calling + Production PatternsThis …
Who Do Autonomous Agents Answer To? The Identity & Governance Problem
Author(s): Manni Arora Originally published on Towards AI. This is Part 2 of a two-part series on Agentic Identity. Part 1: Identity Management for Agentic AI: Making Authentication & Authorization Digestible https://medium.com/towards-artificial-intelligence/identity-management-for-agentic-ai-making-authentication-authorization-digestible-0fc5bb212862 TL;DR Agentic AI challenges traditional IAM by introducing autonomous actors …
Important LLM Papers for the Week From 12/01/2026 To 17/01/2026
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Large Language Models Research Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers must stay informed about the latest …
Time Travel Debugging With Claude Code’s Conversation History
Author(s): Vikas Tiwari Originally published on Towards AI. A few weeks back, I was working on a legacy project that had over 100 microservices. I encountered a bug in this particular backend service that looked really familiar, but I could not recollect …
Kafka vs Kinesis (2026): A Practical Guide to Streaming, Use Cases, Architecture, and Code
Author(s): Rashmi Originally published on Towards AI. Kafka vs Kinesis (2026): A Practical Guide to Streaming, Use Cases, Architecture, and Code Event streaming is the nervous system of modern data platforms: clickstreams, payments, logs, IoT telemetry, fraud signals, and ML features all …
MLflow vs Kubeflow vs Airflow: Choosing the Right MLOps Tool for Real-World Production Systems
Author(s): Rashmi Originally published on Towards AI. MLflow vs Kubeflow vs Airflow: Choosing the Right MLOps Tool for Real-World Production Systems Machine Learning models rarely fail because of algorithms. They fail because pipelines break, experiments are lost, deployments drift, and nobody knows …
How to Denoise Industrial 3D Point Clouds in Python: Advanced Filtering with Vitreous from Telekinesis
Author(s): Telekinesis AI Originally published on Towards AI. For a senior robotics engineer, a raw point cloud from a Zivid, Roboception or Mech-Mind 3D camera is just the starting point. The real challenge is extracting the signal from the noise. In production, …
Your LLM Is Not Broken, Your AI System is🔐
Author(s): Gajanan Tayde Originally published on Towards AI. Your LLM Is Not Broken, Your AI System is🔐 When I first started working with AI systems, security felt… familiar. Models were just another component. You trained them, hosted them behind an API, slapped …
![Why Recommendation Systems Are Structurally Different from Deep Learning [2/2]](https://miro.medium.com/v2/resize:fit:224/1*4nGb2Nmj1E5itLdSpMynSA.png "Why Recommendation Systems Are Structurally Different from Deep Learning [2/2]")
Why Recommendation Systems Are Structurally Different from Deep Learning [2/2]
Author(s): NP_123 Originally published on Towards AI. How DLRM Trades Expressiveness for Structure at Scale This article is Part 2 of a two-part series on the structural and engineering trade-offs behind modern recommendation models such as DLRM. Part 1: https://medium.com/@np123greatest/why-recommendation-systems-are-structurally-different-from-deep-learning-1-2-62e9130acc6e The reference …
4 Coding Pillars Every AI Engineer Should Know About
Author(s): Ahmed Boulahia Originally published on Towards AI. A step-by-step guide to refactoring AI-generated Python scripts into maintainable, professional software for modern SaaS environments. Starting an AI project from scratch can be very overwhelming especially if it is your first time. You …
No Libraries No Shortcuts: Reasoning LLMs from Scratch with PyTorch — Part 2
Author(s): Ashish Abraham Originally published on Towards AI. The no BS Guide to implementing reasoning models from scratch with SFT & RL In Part 1 of this series, we laid the groundwork for understanding how reasoning large language models (LLMs) can be …
DAX Measure Library Architecture: From Messy to Maintainable
Author(s): Gulab Chand Tejwani Originally published on Towards AI. How we stopped wasting $93,600 per year searching for measures we’d already built The Slack message appeared at 2:37 PM on a Tuesday. DAX Measure Library ArchitectureThe article discusses the author’s struggle to …
Unlocking the Magic of Adam: The Math Behind Deep Learning’s Favorite Optimizer
Author(s): Raaja Selvanaathan DATCHANAMOURTHY Originally published on Towards AI. Source: Author At the heart of every deep learning model lies a simple goal: minimizing error. We measure this error using something called a cost Function (or objective function). But knowing the error …
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