ML & AI

Ever launched a machine learning project that worked perfectly in a pilot… and then completely fell apart when you tried to scale it across the enterprise? You’re not alone. In fact, around 87% of enterprise ML projects never make it past the pilot stage. That’s a staggering number—and it’s not because the technology fails. In […]

AI is everywhere. Headlines promise it will run businesses, write code, and even think like humans. But here’s the truth: most of it is overhyped. In this post, I’m going to show you the hidden flaws behind AI’s shiny promises. You’ll see exactly where it fails, why businesses overpay for it, and what to watch

What if I told you 90% of machine learning projects that start with reinforcement learning never make it past a prototype? Meanwhile, most successful AI products you use daily — from spam filters to credit scoring — run on simple supervised learning. That’s the gap we’re closing today. This post isn’t another theory dump. You’ll

You’ve probably heard people say, “AI is about to replace humans.” But is that really true? The truth: AI and human intelligence are completely different—not just in how they work, but in what they even are. Here’s a fact: In 2024, AI solved complex chess problems in seconds, but still can’t understand sarcasm or make

Ever feel like you understand nothing every time you Google “Supervised vs Unsupervised Learning”? You’re not alone.Over 70% of beginner ML learners misinterpret the difference, according to a Kaggle forum poll.And guess what? Most business blogs get it wrong too. Let me give it to you straight — Supervised Learning is for when you know

Think 95% accuracy means your model is great? Think again. In many real-world cases, that same 95% model could be completely useless. For example: in a cancer detection model where only 5% of patients actually have cancer, a model that always predicts “no cancer” scores 95% accuracy — but misses every actual cancer case. Scary,

Why Most Machine Learning Projects Fail Before They Even Start? Because the Engineers underestimate the Importance of Data Quality in Machine Learning Here’s a harsh truth:👉 Nearly 80% of machine learning work is just cleaning and preparing data.👉 Bad data causes more model failures than bad algorithms ever will. Sounds boring? It’s not.Data quality is

Machines are learning faster than ever—and in some cases, they’re outperforming us. From diagnosing diseases to driving cars, machine learning (ML) is starting to do things that once needed a human brain. In 2024, over 40% of businesses already use AI to automate tasks we once thought only people could handle. But here’s the thing: