I’ve seen a lot when I’m working with enterprise AI teams: they nearly always blame the model when something goes wrong. This is understandable, but it’s also frequently incorrect, and it ends up being quite costly. The usual scenario is as follows. The outputs are inconsistent; when someone raises it, the first reaction is to …
The Next AI Bottleneck Isn’t the Model: It’s the Inference System Read More »
Contents# Introduction# What Exactly Is an AI Agent?# Will Data Science Be Replaced by AI in the Future?# What Is the Trend in Data Science in 2026? Shifting to Agentic Workflows# What Will AI Be Like in 2026? Becoming a Collaborative Partner# Conclusion # Introduction The world of data science moves fast. If you are just starting your journey in 2026, …
How AI Agents Will Transform Data Science Work in 2026 Read More »
I in my previous article, “From Code to Insights: Software Engineering Best Practices for Data Analysts”, that engineering skills and best practices can be incredibly useful for analysts and other data professionals. This is even more true now in the AI era, when we have far more opportunities to build our own analytical tools: from …
Contents# Introduction# Setting a Complexity Budget with Textstat# Implementing the LangChain Pipeline# Wrapping Up # Introduction Large language models (LLMs) have a taste for using “flowery”, sometimes overly verbose language in their responses. Ask a simple question, and chances are you may get flooded with paragraphs of overly detailed, enthusiastic, and complex prose. This usual behavior is rooted in …
Guardrails for LLMs: Measuring AI ‘Hallucination’ and Verbosity Read More »
any time in the data engineering world, you’ve likely encountered this debate at least once. Maybe twice. Ok, probably a dozen times😉 “Should we process our data in batches or in real-time?” And if you’re anything like me, you’ve noticed that the answer usually starts with: “Well, it depends…” Which is true. It does depend. But “it …
Batch or Stream? The Eternal Data Processing Dilemma Read More »
Contents# Introduction# Why JSON Wastes Tokens in LLM Pipelines# What TOON Actually Is and When It Is Worth Using# Getting Started with TOON# Final Thoughts # Introduction JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead that does not add much value to the model: …
Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines Read More »
(not that long ago) when being a data scientist meant living in a notebook, tweaking hyperparameters as if your life depended on it, and in a lot of cases, the whole project did, indeed, depend on it. Do you remember those overnight grid searches? Or building feature engineering pipelines that felt more like art than …
Contents# Introduction# Initial Setup# Checking Univariate Normality# Checking Multivariate Normality# Checking Homoscedasticity# Checking Sphericity# Checking Multicollinearity# Wrapping Up # Introduction Anyone who has spent a fair amount of time doing data science may sooner or later learn something: the golden rule of downstream machine learning modeling, known as garbage in, garbage out (GIGO). For example, feeding a linear regression model with highly collinear …
Across 64 English authorities and six 2026 scenarios, even the strongest scenario shock was only 13% of the median uncertainty band. In plain English: the model’s assumptions moved the result less than historical forecast error did. The most aggressive challenger surge I could parameterise sits inside the noise the model has produced in past elections. …
Contents# Introduction# Bootleggers and Baptists# Why the Coalition Works So Well# Learning to Tell Them Apart# Final Thoughts # Introduction Every organization loves to call itself “data-driven.” It’s become the gold standard of credibility, the thing you say to shut down dissent in a meeting. But here’s something worth sitting with for a second: the phrase “according to data analytics” …
Baptists and Bootleggers: The Hidden Coalition Behind ‘Data-Driven’ Decisions Read More »
