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KDnuggets™ is a leading site on Business Analytics, Big Data, Data Mining, Data Science, and Machine Learning and is edited by Gregory Piatetsky-Shapiro (email to editor1 at kdnuggets) and Matthew Mayo (email to mattmayo at kdnuggets).
KDnuggets received over 50 awards/mentions as a leading publication, including
In 20 Data Industry Experts You Need to Follow on Twitter, Churchill Frank, Oct 2017.
In Top 10 Most Influential Brands on Big Data, Onalytica, May 2017.
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No. 1 in Agilience Top Authorities in Machine Learning, Nov 2016.
No. 1 in Agilience Top Authorities for Data Mining, No. 2 for Data Science, Nov 2016.
No. 3 in AI Intelligence & Machine Learning: Top 100 Influencers and Brands, Onalytica, Mar 2016.
No. 4 in Big Data 2016: Top 100 Influencers, Onalytica, Feb 2016.
In InformationWeek Twitter Top 10 Data Science, Analytics, And BI Feeds
among IBM Big Data & Analytics Heroes
In top 30 People in Big Data and Analytics, Innovation Enterprise
profiled in INFORMS: KDnuggets Serves Analytics and Big Data Fields
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featured on Forbes, among Top Influencers in Big Data Source
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| Scope | Trade/B2B, Consumer |
|---|---|
| Language | English |
| Country | United States of America |
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Recent Articles
Search ArticlesBridging Algorithmic Design and Regulatory Standards in Enterprise AI
Your models can be both cutting-edge and compliant if you stop treating governance as the final hurdle and start building it into every stage of the ML pipeline. Your data science team is under increasing pressure to develop more sophisticated machine learning algorithms. Meanwhile, the regulatory landscape around enterprise AI is growing more complex and more restrictive. That is a dilemma for your team when you need space to experiment, but also need to adhere to clear governance rules.
Building AI Agents with Docker Agent
Docker Agent is an open-source, Apache 2.0-licensed CLI plugin built by Docker Engineering, installed and run as docker agent. Its own tagline states the goal plainly: run AI agents like containers. Docker built its reputation on one idea: package software once, run it anywhere, the same way, every time.
7 Best Resources to Learn About Self-Evolving AI Agents
AI agents are quickly moving beyond systems that simply receive an instruction, call a tool, and return an answer. A growing research direction asks a more ambitious question: can an AI agent improve itself through experience? These systems are known as self-evolving (or self-improving / recursively self-improving) AI agents.
OpenAI Dots: The Data Scientist’s Reality Check Original
Dots promises a lot. Before you hand it the keys to your workflow, here's what practitioners need to know. On September 29, OpenAI announced Dots at its DevDay 2026 conference: always-on AI agents that run on their own cloud computers, connect to more than 4,000 apps, and keep working after you close your laptop. It's the most concrete version yet of something the AI industry has been describing for years: an agent that acts more like a coworker than a chatbot.
ML Engineer, AI Engineer, or LLM Engineer: Which Role Actually Builds What in 2026? Original
Open three job boards and search "AI." One company calls the role AI Engineer. Another calls it Applied AI Engineer. A third calls it LLM Engineer. The listed responsibilities look almost identical: Python, an API key for a language model, some mention of retrieval, a line about "production reliability." Look closer, and the specifics move too. One posting wants LangChain experience. Another wants fine-tuning experience with LoRA.
I Tested 5 AI Coding Assistants for a Month: Here’s What I Actually Found Original
The pitch is always the same: describe what you want, watch the code appear, ship faster. After a month of putting five of the most-discussed AI coding assistants through real work — a legacy refactor, a greenfield API build, and a few debugging sessions I'd rather forget — the honest answer is messier than any vendor demo suggests. Each tool carries a different philosophy about what AI assistance should look like. Cursor wants to replace your entire editor.
Who Pays for AI Data Centers?
The fight is usually framed as whether data centers get built. The harder question underneath it is "who pays and who decides?" The costs are intertwined. John Steinbach has lived in his Manassas, Virginia, home for nearly 40 years, and in January 2026 he opened an electricity bill for \$281, up from roughly \$100 the month before. "It's just so far beyond any bill that I've ever had," he told Consumer Reports.
5 Best Practices for Building Robust Python AI Libraries Original
This article covers building robust Python AI libraries specifically, Python AI SDK best practices, and what separates a production-ready AI package from one that only survives in its own demo. A well-loved open-source AI wrapper works perfectly in the maintainer's demo notebook. A week after someone else adopts it, three things break in three different ways. A missing API key crashes with a bare instead of a message anyone could act on.
Meta Muse Explained: What It Is, How It Works, and What It Can Do Original
By, KDnuggets Technical Editor & Content Specialist on October 5, 2026 in Artificial Intelligence Meta has entered the AI agent race in a big way. On September 8, 2026, Meta launched Muse, a personal AI agent designed to do more than just answer questions. Muse can browse websites, connect to your apps, send emails, make purchases, fill out forms, manage longer-running goals, and continue working even after you close the app.
3 Statsmodels Tricks for Time Series Analysis & Forecasting Original
A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it. A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it. The point forecast is the smallest task it can perform. Every trick runs from a case of asking the results object for something it has already worked out, rather than having to rebuild that thing by hand. One dataset, one model, three methods people routinely reimplement.