The Essential Guide to ML Evaluation Metrics for Regression
Author(s): Ayo Akinkugbe Originally published on Towards AI. Photo by Europeana on Unsplash Introduction Machine learning models are only as good as our ability to measure them. Though a perfect model isn’t always possible, a good enough model is. But how do …
SEO to GEO: How Data Scientists Can Thrive in an AI-First World
Author(s): Qaisar Tanvir | AVP – AI/ML Architecture and MLOps Originally published on Towards AI. SEO to GEO — Search Engine Optimization in the age of Gen AI Content strategy is undergoing a fundamental shift: traditional SEO, with its keyword-centric, link-building tactics, …
Introducing Our New Course: AI for Business Professionals
Author(s): Towards AI Editorial Team Originally published on Towards AI. The message from leading companies is unmistakable: AI is now an existential competitive challenge. Used effectively, AI significantly enhances productivity and quality. But there’s a catch — when applied poorly or unofficially, …
What Is Overfitting in Machine Learning?Overfitting in ML Explained Simply with Real Examples | M005
Author(s): Mehul Ligade Originally published on Towards AI. What Is Overfitting in Machine Learning?Overfitting in ML Explained Simply with Real Examples | M005 📍 Abstract If you have ever trained a machine learning model that gave you a perfect-looking score — and …
The Essential Guide to Model Evaluation Metrics for Classification
Author(s): Ayo Akinkugbe Originally published on Towards AI. Photo by Europeana on Unsplash Introduction Classification is one of the most common machine learning tasks, where models predict discrete categories or classes. Examples include detecting fraud, diagnosing diseases, or filtering spam emails. To …
Retrieving Structured Output From MCP-Integrated LangGraph Agent
Author(s): Ruiwen (Rei-1) Originally published on Towards AI. Structured output transforms LLM-based applications and agentic systems into reliable, interoperable components by enforcing a clear, machine-readable schema (e.g., JSON with explicitly defined fields). This makes it easy for downstream systems — dashboards, databases, …
TAI #155: DeepSeek R1’s Reasoning Leap & Unlocking AI’s Untapped Potential at Work
Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie This week marked a strong return for the open-source community, with DeepSeek’s R1–0528 update significantly narrowing the performance gap with state-of-the-art reasoning models. Despite …
Introduction to RAG: Basics to Mastery. 3-Agentic RAG-Giving Your Retrieval Pipeline a Brain
Author(s): Taha Azizi Originally published on Towards AI. Part 3 of the mini-series introduction to RAG Introduction In the last two articles, we built: A basic RAG Pipeline with semantic search. A Hybrid RAG that combined keyword + semantic search. Now we’re …
The Anchor That Almost Was
Author(s): Erez Azaria Originally published on Towards AI. Recently, I read Jonathan Zittrain’s article in The Atlantic, describing how his name “breaks” ChatGPT. Being curious, I tried to see if the model could provide some data on the ‘kill switch’ mechanism mentioned …
Fine-Tuning VLLMs for Document Understanding
Author(s): Eivind Kjosbakken Originally published on Towards AI. In this article, I discuss how you can fine-tune VLMs (visual large language models, often called VLLMs) like Qwen 2.5 VL 7B. I will introduce you to a dataset of handwritten digits, which the …
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