Towards Data Science @TDataScience
The world's leading publication for data science and artificial intelligence professionals. Submit an Article ✍️ https://t.co/57pIMegK1o towardsdatascience.com We're Global 🌏 Joined October 2016-
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The old AI workflow was simple: ask a question, get an answer, and move on. Gursimar Singh examines why productive AI agents demand a different approach, where delegation and review matter more than writing the perfect prompt. towardsdatascience.com/stop-using-ai-…
If you'd like to tinker with open-source decision models and to make informed choices among them (and between decision models and generative LLMs), Lambert Leong presents a thoughtful comparison of their performance across several key parameters. towardsdatascience.com/jev-your-engin…
"But this speed also creates a new problem: when code is produced quickly without a clear structure, the project can become difficult to understand, maintain, and extend." @erikapatg explains why data scientists still need to have a deep understanding of software-engineering principles in the age of AI coding agents. towardsdatascience.com/a-scaffold-at-…
"Every site applies the resulting global mask to its columns, and ordinary federated training such as FedAvg runs on the reduced table. No rows or gradients leave a site during the election." Ioannis Christofilogiannis patiently walks us through his work on federated feature selection. towardsdatascience.com/voting-on-feat…
How do words become related in a learned-vector space? Follow along @Nikhil_on_ds's accessible explainer to expand your understanding of word embeddings. towardsdatascience.com/how-do-word-em…
"Ensembling is close to gospel in competitive ML: stack a handful of models, feed their predictions into a meta-model that learns how to combine them, and you generally beat any single model in the stack." @Himansh70809561 takes a closer look to assess the benefits of model stacking. towardsdatascience.com/does-stacking-…
In a thoughtful new explainer, @mangal_utkarsh explains why LLMs can't perform deterministically even when their temperature is set to 0, and explores why identical completions fall apart after about a hundred tokens. towardsdatascience.com/why-temperatur…
"Most attacks happen when AI agents are exposed to the lethal trifecta. If agents can read from untrusted sources, access internal knowledge, and communicate to the outside world, they are vulnerable." @Thuwarakesh unpacks the areas and workflows where you should add safety guardrails to your agentic systems. towardsdatascience.com/how-can-ai-age…
From a standard ReAct (Reason + Act) loop to autonomous swarms, Partha Sarkar digs into the nitty-gritty details of working with advanced agentic architectural patterns. towardsdatascience.com/agentic-system…
Is there *already* a new, powerful rival to Jev? @taupirho takes a close look at OpenAI's new Decisions API and examines its performance on several potential use cases. towardsdatascience.com/a-practical-gu…
How well do AI image judges perform, and what are the areas where they still struggle? @DattijoMuhammad explores a complex set of challenges in his new deep dive on vision AI models. towardsdatascience.com/i-built-a-trap…
"On paper, your name is on the commit. In practice, you only own that code if you can trace its logic and patch it completely unaided." @emmimalpa digs into an issue many practitioners face these days: their shaky sense of ownership over the code they had AI generate for them. towardsdatascience.com/do-you-still-o…
Walking us through the process of building a claim-checker for writing with Codex, @BenjaminNweke11 unpacks the challenges of tracing the moments where rule-based checks break down. towardsdatascience.com/i-built-a-clai…
Having previously benchmarked the Python 3.14 JIT on three workloads, @taupirho turns to the latest version's just-in-time compiler to measure in what areas performance has improved. towardsdatascience.com/i-reran-my-pyt…
In his just-launched series, Jarom Hulet explores the fundamentals of deploying data science tools, walking readers through the building blocks and essential components of getting your code to run anywhere. towardsdatascience.com/deploying-data…
The Python 3.14 Just-In-Time (JIT) compiler was good, but the new Python 3.15 JIT is even better. Find out how much it's improved it is as I take the new JIT for a spin and compare it to its predecessor in my new @TDataScience article. track.pstmrk.it/3s/towardsdata…
"Ten years later I design data architectures for agentic AI systems in production, and recently I re-read our patent with today's eyes. The experience was uncanny." Mario Noioso revisits his 2016 IBM patent and reflects on its similarities to today's AI agents. towardsdatascience.com/we-specified-a…
From customer churn prediction and anomaly detection to pricing elasticity, Alex Davis walks us through a wide range of product data science tasks where NLP still reigns supreme. towardsdatascience.com/how-to-leverag…
Partitioning with nearly one assignment per moved value, instead of Hoare's one and a half By Tigran Hayrapetyan towardsdatascience.com/cyclic-partiti…
"When an AI coding agent takes extra turns to get to a passing patch, how much of that work was actually needed? And can a workflow around the agent reduce the unnecessary work?" @emmimalpa turns to an emerging challenge for practitioners: agentic-system efficiency. towardsdatascience.com/coding-agents-…
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