data science
data science on Beyond Market Intelligence: a running collection of 211 stories we have gathered and hand-picked because they are worth your time. Every post here touches on data science in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around data science, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Build and Run an Intelligent Document Processing (IDP) System in the Cloud
Unlock streamlined data management with an Intelligent Document Processing (IDP) system, now accessible in the cloud. This guide details building and running a solution on AWS to automate the classification and extraction of Personally Identifiable Information (PII) from emails – a critical step for compliance and efficiency. Discover how to transform unstructured data into actionable insights, empowering your workflows. For a deeper dive into the foundation models underpinning such systems, explore "Tabular LLMs: An Introduction" on our site.
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
Loop Engineering presents a compelling approach to Retrieval-Augmented Generation (RAG) with its LLM Cascade, detailed in "Loop Engineering for RAG Generation." This innovative strategy sequences language models, starting with cost-effective local models and scaling up to a hosted flagship, optimizing both expense and accuracy. The research validates this cascade through rigorous testing—a sweep of twenty local models compared against a flagship—highlighting two key benefits: cost efficiency and a robust validation loop.
Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet
Tabular foundation models represent a significant shift in data management. These innovative models predict missing spreadsheet columns zero-shot—akin to how large language models complete text—and are rapidly surpassing traditional gradient-boosted trees on benchmarks like TabArena. Our exploration details how these models function, features an independent reproduction of a leading open-source implementation, and clarifies where XGBoost maintains its edge. For a deeper dive into AI assistants, consider exploring "Bluesky’s AI assistant Attie expands into an open social research tool."
Context Windows Forget What Matters — I Built a Usage-Reinforced Decay Engine for AI Agent Memory
Most AI memory systems prioritize recency, potentially overlooking critical information. A new approach, detailed in a *Towards Data Science* article, leverages the Ebbinghaus forgetting curve to build a usage-reinforced decay engine for LLMs, enhancing AI agent memory. This innovative system prioritizes retaining the most impactful data, rather than simply the most recent. Explore how this technique addresses a key limitation in current AI architectures—a challenge also explored in articles like "AegisAI, founded by former Google security execs, lands $36M to stop AI-driven spear phishing."
When Data Science Makes Us Sad: The Story of an Overbooked Flight
Data science isn't always a victory. Sometimes, it highlights uncomfortable truths, as revealed in "When Data Science Makes Us Sad: The Story of an Overbooked Flight." This compelling piece explores a real-world scenario where algorithmic decisions resulted in an $8 million payout versus a potential $5,000 resolution—and the possibility of significant public backlash. Discover how seemingly rational data models can lead to unexpected, and costly, outcomes. For a deeper dive into optimizing AI performance, explore "Prompt Compression Techniques."
Lessons Learned After 8.5 Years of ML
After 8.5 years immersed in machine learning, certain core principles consistently emerge. Patience is paramount; progress isn't always linear. Optimism fuels exploration, while discipline ensures rigorous execution. Successful ML isn’t solely about algorithms—it’s about well-defined projects and high-performing teams. These lessons underscore the importance of a grounded, iterative approach. For a deeper dive into practical challenges, consider "Most RAG Hallucinations Are Extraction Errors," which highlights critical error identification in retrieval-augmented generation systems.
Why Adding More AI Agents Made Our System Slower
Scaling AI agent systems isn’t always linear. We recently encountered a surprising bottleneck: asynchronous task management. As we expanded to hundreds of LLM agents, seemingly minor CPU tasks quietly became our largest performance constraint, slowing overall system speed. This post details how we identified and addressed this hidden cost, offering practical insights for anyone building complex AI workflows. Learn from our experience – a challenge we’ve explored further, alongside broader lessons from 8.5 years of machine learning.
Anyone heading to Jeju for KDD? Let's meet up! 🙋[D]
Heading to KDD in Jeju? Let’s connect! We'd love to meet fellow attendees exploring the frontiers of AI. Specifically, we’re keen to engage with those focused on interpretability, fairness, and the editing of text-to-image models—though conversations on any topic are welcome. If you're interested in learning more about iterative RAG generation approaches, check out our recent article, "Loop Engineering for RAG Generation." We land on the 8th and invite you to reach out for coffee, discussion, or simply to share experiences.
Loop Engineering for RAG Generation: iterate top-k one at a time
Loop Engineering’s latest research explores a refined approach to Retrieval-Augmented Generation (RAG), detailing “iterate top-k one at a time” for enhanced Enterprise Document Intelligence. This methodology introduces two distinct regimes for candidate selection and a crucial "sufficiency signal" to optimize generation. Notably, per-question type dispatch significantly reduces processing costs. This innovative technique represents a crucial step towards more efficient and intelligent AI applications—a shift mirrored by companies like Monday.com, who are streamlining operations to focus on AI.
Build an LLM Agent That Can Write and Run Code
Unlock the potential of AI-powered code generation and execution. This hands-on walkthrough guides you through building an LLM agent using the OpenAI Agents SDK and Docker. Learn to empower your workflows by seamlessly integrating code writing and running capabilities. We’ll demonstrate a practical approach to leveraging these tools, offering a future-focused solution for data professionals. For those interested in a deeper dive into LLM runtimes, explore "How To Build Your Own LLM Runtime From Scratch" for a comprehensive understanding of the underlying infrastructure.
How To Build Your Own LLM Runtime From Scratch
Ever wondered what it takes to build an LLM inference runtime from the ground up? This comprehensive guide details that journey, walking you through the creation of a small runtime called annotated-llm-runtime, all while running on an H100. We explore the intricacies of managing weights and CUDA graphs, highlighting three key bugs that shaped the development process. Delve into the complexities of AI infrastructure—as explored further in "OpenAI’s AI spending spree has ballooned to $750B"—and empower yourself with a deeper understanding of LLM technology.
Detecting Vulnerabilities in Agent Skills with SkillSpector: From Green Checkmark to Real Security Judgment
Static analysis tools offer a first line of defense, but detecting vulnerabilities in AI agent skills requires more than just automated checks. Our latest post, "Detecting Vulnerabilities in Agent Skills with SkillSpector," explores this critical gap, highlighting how SkillSpector moves beyond simple “green checkmark” assessments. We demonstrate how static analysis can identify malicious skills while often over-flagging useful ones, revealing the crucial role of human judgment in making informed security decisions.
10 Newsletters Keeping You Ahead in AI
Staying ahead in the rapidly evolving world of AI can feel overwhelming. Cut through the noise with our curated list of 10 essential newsletters—your reliable guide to daily news, technical research, policy developments, and invaluable builder tools. We’ve assembled resources that empower informed decision-making and strategic exploration. For a deeper dive into securing AI workloads, explore our recent article, "GKE Security Blueprint Joins Growing List of Cloud AI Frameworks," and discover practical steps for safeguarding your AI initiatives.
Are Your ML Experiments a Mess? Here’s the Fix
Are your machine learning experiments feeling disorganized? Reproducibility and efficient tracking are critical for progress, yet often overlooked. This hands-on guide delivers a practical fix: MLflow. Discover how to streamline experiment tracking, meticulously log models, and reliably reproduce results, empowering your data science workflows. Learn to navigate the complexities of ML development with clarity and confidence. For a deeper dive into related challenges, explore "Yelp Unifies ML Model Training with Training Orchestrator" and unlock further insights.
Water Cooler Small Talk, Ep. 12: Byzantine Fault Tolerance
Welcome to Water Cooler Small Talk, where we tackle complex concepts with approachable clarity. In this episode, we delve into Byzantine Fault Tolerance – a surprisingly relevant challenge in today’s distributed systems and, frankly, life. How do you reach consensus when you can't guarantee the trustworthiness of everyone involved? Explore this fascinating solution, vital for everything from blockchain to critical infrastructure, and discover how it addresses scenarios where malicious actors or simple errors can disrupt decision-making.
Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
Loop Engineering presents a progressive approach to enterprise document intelligence, demonstrating Adaptive Parsing in action. This initial installment, "Parsing Flat Tables with Azure and Figures with a Vision LLM," explores utilizing Large Language Models (LLMs) as a critical last line of defense. We detail two complete escalations: extracting data from flat tables via Azure and interpreting figures through a vision model. For those seeking to optimize agent performance, consider "How to Run Claude Code Agents for 24+ Hours" for deeper insights into long-running coding agents.
How to Run Claude Code Agents for 24+ Hours
Unlock sustained coding productivity with Claude Code Agents running continuously – even for 24+ hours. This guide explores how to leverage these powerful AI assistants to streamline your engineering workflows and tackle complex projects with unprecedented efficiency. Discover practical techniques for maintaining and optimizing long-running agents, transforming your coding process. For a foundational understanding of setup and configuration, see "A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming" and elevate your agentic programming skills.
Automatically Assign a Category to Uncategorized Rows in Power Query and DAX
Categorized data is foundational for effective reporting and analysis; uncategorized rows hinder grouping and aggregation. When faced with data lacking assigned categories, establishing rules for assignment becomes essential. This post explores a practical solution for automatically assigning categories to uncategorized rows, demonstrated through a facility management project using Power Query and DAX. Discover how this approach unlocks deeper insights from your data. For further exploration of related techniques, see "TabFM Studio" and its application to spreadsheet predictions.
Your AI Agent Passed Every Eval. Finance Still Killed It.
A recent evaluation revealed a surprising paradox: an AI agent flawlessly passed every metric in our published harness, demonstrating impressive capabilities. However, the finance department ultimately halted its deployment. While the agent resolved issues effectively, the cost of those resolutions exceeded the expense of human counterparts—a critical factor in practical application. This highlights a crucial consideration for AI adoption, as explored further in "Kimi: Threat or menace?" Demonstrating technical success doesn’t guarantee financial viability.
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Optimizing Retrieval-Augmented Generation (RAG) systems hinges on precise question parsing. Loop Engineering for RAG, detailed in our latest Enterprise Document Intelligence report [Vol.1 #6quinquies], introduces a streamlined approach: a deliberately small loop focused on question refinement. This involves reading the document, identifying gaps, and re-parsing the query—a critical step before retrieval. Explore this technique to enhance accuracy and efficiency. For a foundational understanding of iterative learning processes, consider “Backpropagation Explained for Beginners (Part 1).”
Backpropagation Explained for Beginners (Part 1): Building the Intuition
Unlock the learning process behind neural networks with our introductory guide to backpropagation. This first installment focuses on building intuition—understanding *how* these powerful systems adjust to improve their performance, step by step. Forget complex equations for now; we'll prioritize a clear, accessible explanation of the core concepts. If you’re intrigued by the broader implications of AI development, consider exploring "Nonprofit Current AI is racing to build the World Wide Web of AI, free for all," for a glimpse into a future where AI benefits everyone.
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
Many companies are leveraging AI, yet few possess a practical architecture for an AI-native enterprise data platform. Building one demands more than isolated AI tools; it requires a cohesive system. Our latest article explores a robust architecture featuring data agents for streamlined integration, AI-powered quality assurance, and essential AI governance. Discover how to move beyond experimentation and establish a foundation for scalable, reliable AI initiatives. For related insights on structuring data for AI agents, see Pinecone’s introduction of Nexus Engine.
KDnuggets Weekly Roundup: Week of July 13, 2026
This week’s KDnuggets Weekly Roundup delivers practical insights for data professionals. We're prioritizing efficiency, starting with a clear alternative to cumbersome if-else chains in Python – embrace the Registry Pattern. Level up your portfolio with five real-world SQL projects, stay current with ten top AI YouTube channels, and explore structured language model generation. For deeper exploration of related topics, consider "Pinecone Introduces Nexus Engine," now generally available, for compiling business context into structured data for AI agents.
Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It
Loop Engineering’s adaptive PDF parsing offers a transformative approach to document intelligence. Start with a cost-effective parser and only escalate to heavier processing when a page demands it—ensuring you pay only for what you need. This innovative system incorporates an escalation cascade and deterministic checks, proactively flagging parse failures *before* incurring deeper processing costs. Discover how this model delivers efficiency and predictability for enterprise document workflows, as explored in detail in our Enterprise Document Intelligence series.