Building ML in the Dark: A Survival Guide for the Solo Practitioner
Author(s): Yuval Mehta Originally published on Towards AI. Photo by Boitumelo on Unsplash No GPU cluster. No data team. No ML platform. Here’s what actually ships. Most ML content is written for teams that have things. A labelled dataset. An MLOps platform. …
Data Mining
Author(s): Sefa Bilicier Originally published on Towards AI. Introduction In today’s digital economy, data has become the new oil. But unlike oil, which requires drilling and refining, data requires a different kind of extraction: data mining. Everyday, organizations generate massive amounts of …
Part 16: Data Manipulation in Data Validation and Quality Control
Author(s): Raj kumar Originally published on Towards AI. Data quality issues are the silent killers of production systems. A single malformed record can crash your pipeline. A gradual drift in data distributions can slowly degrade model performance. Missing values that sneak through …
AgentOps: Your AI Agent Is Already Failing in Production. You Just Can’t See It
Author(s): Divy Yadav Originally published on Towards AI. The practical guide to monitoring, debugging, and governing AI agents before they become a liability You shipped an AI agent. It worked in staging. Photo by authorFollowing the introduction, the article delves into the …
Meta Just Built an AI That Rewrites the Rules of How It Gets Smarter. Then It Rewrote Those Rules Too.
Author(s): DrSwarnenduAI Originally published on Towards AI. The complete breakdown of HyperAgents — what metacognitive self-modification actually means, why the old way always hits a ceiling, and the result that made the AI safety community sit up straight. Meta Just Built an …
LLM Benchmarks Are Junk Science
Author(s): Kaushik Rajan Originally published on Towards AI. An Oxford review of 445 benchmarks found 84% lack basic statistical testing. Models score 90% on standard tests but 2% on unseen problems. A 5-question smell test for any benchmark claim. Over the past …
I Passed the DP-600 Fabric Analytics Engineer Exam — Here’s My Honest Study Plan (With What I’d Skip)
Author(s): Sheth Priyanka Originally published on Towards AI. Six weeks, two failed practice runs, one embarrassingly wrong assumption about what the exam actually tests, and the exact study approach that finally got me there. No sponsored course recommendations. No affiliate links. Just …
We Gave ChatGPT Our Raw Sales Data and Asked It to Build a Dashboard. A Senior Analyst Reviewed the Results.
Author(s): Gulab Chand Tejwani Originally published on Towards AI. We uploaded 14 months of real client sales data — 127,000 transactions, 8 product categories, 12 regions — to ChatGPT and asked it to build a complete analytics dashboard. Then I sat down …
Part 9: Data Manipulation in Data Merging and Joins
Author(s): Raj kumar Originally published on Towards AI. Every analysis that combines data from multiple sources faces the same fundamental question: how should these datasets align? Which records match? What happens when they don’t? These aren’t just technical decisions. They shape what …
Part 8: Data Manipulation in Grouping and Aggregation
Author(s): Raj kumar Originally published on Towards AI. Every business decision starts with a question. What are our total sales by region? Which product categories generate the most revenue? How do customer segments compare in profitability? These questions all share something in …
Part 7: Data Manipulation in Date and Time Handling
Author(s): Raj kumar Originally published on Towards AI. Time is the invisible thread that runs through almost every dataset you’ll encounter. Sales happen on specific dates. Transactions occur at precise moments. Events unfold across hours, days, and years. Yet despite how fundamental …
MCP (Model Context Protocol): Explained Simply
Author(s): Nisarg Bhatt Originally published on Towards AI. Here is something that does not get talked about enough. The AI tools you use every day, including ChatGPT, Claude, Cursor, whatever your favourite is, they all have the same quiet limitation. They know …
From Monolith to Microservices: A Developer’s Survival Guide in 2026
Author(s): FutureLens Originally published on Towards AI. From Monolith to Microservices: A Developer’s Survival Guide in 2026 The journey starting from monolithic architecture to microservices is a very big challenging nowadays yet rewarding one. As we are explore in the ins and …
The Video Frontier: When AI Stopped Watching and Started Understanding
Author(s): Ampatishan Sivalingam Originally published on Towards AI. Part IV of the Multimodal Intelligence Series · The model learned to see. Then it learned to remember what it saw. This stack did not exist in 2023. The U-Net diffusion models that produced …
LLMOps Guide: The End-to-End Pipeline for Reliable AI Applications
Author(s): Divy Yadav Originally published on Towards AI. For developers who have just built an LLM, RAG, or agentic system and are wondering what comes next. Most teams celebrate when their AI application finally works. The demo looks good, the feature ships. …
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