#towards-data-science

Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph The post Building Human-In-The-Loop Agentic Workflows appeared first on Towards Data Science.

Proactivity, blocking, and planning The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science.

How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and efficiency The post The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026 appeared first on Towards Data Science.

We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same rigor around proving they work. The post Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation appeared first on Towards Data Science.

How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making The post From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI appeared first on Towards Data Science.

Supercharge Claude Code with continual learning The post How to Make Claude Code Improve from its Own Mistakes appeared first on Towards Data Science.

Data Leakage, Real-World Models, and the Path to Production AI in Healthcare The post My Models Failed. That’s How I Became a Better Data Scientist. appeared first on Towards Data Science.

Rapid prototyping with Replit, AI agents, and minimal manual coding The post I Built a Podcast Clipping App in One Weekend Using Vibe Coding appeared first on Towards Data Science.

This Article asks what happens next. The model has encoded its knowledge of fraud as symbolic rules. V14 below a threshold means fraud. What happens when that relationship starts to change? Can the rules act as a canary? In other words: can neuro-symbolic concept drift monitoring work at inference t

Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison matrix, and Python workflow to fix it with causal inference. The post Causal Inference Is Eating Machine Learning appeared first on Towards Data Science.

Master data types, index alignment, and defensive Pandas practices to prevent silent bugs in real data pipelines. The post 4 Pandas Concepts That Quietly Break Your Data Pipelines appeared first on Towards Data Science.

A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a bird's wing The post Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow appeared first on Towards Data Science.

A step-by-step guide to making your OpenAI apps faster, cheaper, and more efficient The post Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial appeared first on Towards Data Science.

Most data platforms don’t break overnight; they grow into complexity, query by query. Over time, business logic spreads across SQL scripts, dashboards, and scheduled jobs until the system becomes a “SQL jungle.” This article explores how that happens and how to bring structure back. The post Escapin

Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP The post A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations appeared first on Towards Data Science.

You already think like a Bayesian. Your stats class just taught the formula before the intuition. Here's a 5-step framework to apply it at work. The post Bayesian Thinking for People Who Hated Statistics appeared first on Towards Data Science.

What I learned building and distributing my first Skill from scratch The post How to Build a Production-Ready Claude Code Skill appeared first on Towards Data Science.

Shadow AI and the desire paths of modern work The post Follow AI Footpaths appeared first on Towards Data Science.

It’s a feature of the architecture The post Hallucinations in LLMs Are Not a Bug in the Data appeared first on Towards Data Science.

Most neuro-symbolic systems inject rules written by humans. But what if a neural network could discover those rules itself? In this experiment, I extend a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during training. On the Kaggle C