data science
data science on Beyond Market Intelligence: a running collection of 209 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.
Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline
Retrieval-Augmented Generation (RAG) systems rely on core components delivering consistent results, but what happens when those components falter? Loop Engineering addresses precisely that—the often-overlooked work performed *between* those core steps. This first installment of Enterprise Document Intelligence explores the critical control surfaces—trigger, termination, and recovery—that ensure a RAG pipeline remains productive, even when faced with retrieval misses or API timeouts. Discover how these “small loops” safeguard against common failures, building on insights from articles like "How to Perform Effective Project Management with AI."
Designing a Persistent Knowledge Layer That Refuses to Guess
Traditional Retrieval-Augmented Generation (RAG) struggles with a fundamental limitation: it retrieves but doesn’t remember. Our blueprint, "Designing a Persistent Knowledge Layer That Refuses to Guess," offers a vendor-neutral solution for applications requiring accumulated understanding. This comprehensive guide details a complete Azure-native implementation—leveraging Microsoft Foundry, Azure AI Search, Cosmos DB, and FastAPI—demonstrated with a property-insurance corpus. Explore how building a persistent knowledge layer elevates RAG beyond simple retrieval, ensuring contextually relevant and consistently informed responses.
Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
Explore a novel approach to data processing with "Running SQL Concurrently Across Three Remote DuckDB Servers with Quack." This experiment demonstrates a practical application of remote SQL execution, empowering users to leverage distributed resources for enhanced performance. Discover how Quack facilitates this process, offering a streamlined solution for complex queries. For those interested in building applications that accumulate understanding, consider "Designing a Persistent Knowledge Layer That Refuses to Guess," which details a vendor-neutral blueprint for RAG systems.
How to Shine as a Data Scientist in the Vibe Coding Era
The rise of AI coding tools like those explored in "How to Install Codex CLI" signals a significant shift for data scientists. Coding proficiency is increasingly becoming a commodity; the future belongs to those who leverage these tools strategically. This post outlines how to thrive in this "Vibe Coding Era," focusing on higher-level skills like problem framing, insightful analysis, and communicating data-driven narratives. Discover how to evolve beyond coding and become the indispensable data scientist of tomorrow.
Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming
The landscape of mathematical experimentation is rapidly evolving, driven by the power of human-machine collaboration. Recent breakthroughs demonstrate this potential: two significant open problems—exact-arithmetic checking and the development of a proof assistant—were tackled and advanced over a single weekend through this synergistic approach. This signals a future where AI tools significantly accelerate research. For those seeking to leverage AI assistance directly, explore "How to Install Codex CLI: A Step-by-Step Guide" to begin your journey.
If you had a bunch of GPUs lying around, what would you actually build with them? (Running LLMs is off the table) [D]
Beyond the well-trodden path of local LLMs, a stack of high-end GPUs unlocks a realm of compelling possibilities. What truly innovative projects would emerge? Consider distributed simulations, specialized generative models outside of text, or accelerated rendering pipelines. The opportunity exists for impactful homelab experiments demanding serious computational power, or even uniquely ambitious personal endeavors. Explore the potential – as demonstrated by projects like the Doom renderer reimagined as a transformer, discussed in "I compiled Doom's renderer into a 21B-parameter transformer"—and share your most intriguing ideas.
Are there any theoretically-guided practices left in machine learning nowadays? [D]
The rise of large language models has sparked a critical question: have theoretically-guided practices in machine learning become relics of the past? Historically, principles like avoiding overfitting, rigorous test set separation, and optimizer selection based on performance guarantees shaped model development. However, recent empirical successes suggest these guidelines are often superseded by what simply *works*. Has the field transitioned to a purely empirical approach, driven by observed results rather than foundational theory?
My Model Was Cheating on Its Own Test
Data scientists often strive for model accuracy, but what happens when a model gains an unfair advantage? In a recent *Towards Data Science* post, an author discovered their car price prediction model was "cheating" – a preprocessing pipeline inadvertently allowed it to glimpse the test set. This resulted in a deceptively high R-squared score. The experience highlights a critical pitfall in machine learning workflows and the importance of rigorous validation.
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Unlock the next level of Retrieval-Augmented Generation (RAG) with our latest exploration of Loop Engineering and the Dispatcher pattern. Enterprise Document Intelligence, Vol. 1 #13, details a crucial advancement: intelligently controlling when to loop and when to stop within a RAG workflow. This approach defines what “agentic RAG” *should* look like, moving beyond simplistic iterations. Discover how this architecture puts patterns together for more efficient and reliable results.
A Day in the Life of a Data Scientist in 2026
The role of the data scientist is undergoing a profound transformation. In "A Day in the Life of a Data Scientist in 2026," we explore how AI has fundamentally reshaped daily workflows, moving beyond traditional spreadsheet limitations. Discover how automation, intelligent insights, and streamlined model deployment now define the modern data scientist's experience. This post offers a future-focused perspective on leveraging AI to empower data-driven decision-making—a shift that's already underway, as highlighted by innovations like Kog’s work to optimize GPU inference for agentic workflows.
I Made an LLM Lay Siege to My Minecraft House
Can a language model actively design a challenging Minecraft level? We put it to the test, tasking an LLM with laying siege to a player-built house – a compelling experiment in adversarial level design. The results are surprisingly dynamic and reveal the potential for AI to generate complex, reactive environments. Explore the full story and see how this experiment unfolded. For further insights into AI agents, consider "5 Fun Agentic AI Papers to Read," offering a curated selection of foundational research.
LangChain vs LangGraph: 4 Key Differences and When to Use Each
Navigating agentic workflows demands the right tools. LangChain and LangGraph are both vital for building AI systems, but understanding their differences is key to optimal performance. This guide delivers a practical comparison, outlining 4 key distinctions to empower your decision-making. Discover when to leverage LangChain’s versatility versus LangGraph’s focused approach to graph-based agent design. For deeper insights into knowledge exchange within LLMs, explore "How to Utilize OKF Efficiently."
Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model
Enterprise RAG pipelines often introduce unnecessary latency by repeatedly calling Large Language Models (LLMs). Article 9 explores a practical solution: strategically bypassing the LLM for straightforward queries. By implementing a simple keyword-based routing signal, organizations can achieve significant reductions in both latency—approximately two seconds per question—and operational costs. This approach demonstrates that optimizing LLM usage, not simply upgrading models, is key to efficient Enterprise Document Intelligence. Discover further insights into knowledge exchange with "How to Utilize OKF Efficiently."
How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs
Unlock seamless knowledge exchange between AI agents with Google’s Open Knowledge Format (OKF). This post demonstrates a practical application—facilitating efficient data transfer between three Qwen2.5-Coder models—achieving a significant 28–37% reduction in time-to-first-token (TTFT) and ensuring data integrity through full-vocabulary equivalence checks. Explore how OKF's Markdown+YAML structure empowers streamlined agent collaboration. For further insights into optimizing AI agent costs, consider "Writer says its new Palmyra X6 model cuts AI agent costs by 52%."
Building Multimodal Workflows with a Local LLM
Unlock new possibilities in data processing by building multimodal workflows directly on your machine. This post explores leveraging Gemma 4 and Ollama to create powerful systems capable of accepting image inputs and generating structured outputs – a significant step beyond traditional spreadsheet limitations. Discover how local LLMs empower accessible and future-focused data manipulation. For a foundational understanding of the underlying mechanics, explore "Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works," to deepen your knowledge of the neural networks at play.
How to Place Vertiport Locations in Any City Using Geospatial Machine Learning
Optimizing vertiport placement is critical for the successful rollout of urban air mobility. Our latest case study demonstrates a reproducible methodology for identifying ideal locations within any city, leveraging geospatial machine learning. Using Lagos, Nigeria as a practical example, we analyze population density, existing transport infrastructure, and crucial airspace constraints to pinpoint optimal sites. Discover how to transform urban planning with data-driven insights—a future-focused approach to integrating vertical takeoff and landing.
Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works
## Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works Ready to understand the core of neural network training? This post dives into how backpropagation truly functions, moving beyond the initial concept to explore the cascade of gradients. We'll break down the process of calculating gradients from a single point to every parameter, illuminating how this iterative refinement shapes model learning. For a deeper dive into the broader context of data intelligence and decision-making, see "Before Full Agentic RAG.
Building an End-to-End Data Science Portfolio Project
Most data science portfolios showcase a notebook—a good start, but often incomplete. Elevate yours by building a truly end-to-end project, demonstrating practical application beyond isolated analysis. This guide empowers you to transform your skills into a compelling, real-world showcase. Discover how to deploy models, manage data pipelines, and present your work professionally. As a reminder, understanding statistical significance is critical; consider our article, "Stop Calling the First Significant Day a Win," for deeper insights into A/B testing best practices.
Can a Local LLM Run My AI Assistant?
Can a local Large Language Model (LLM) truly replace cloud-based AI assistants like Claude? We put that question to the test, replaying 27 real-world production tasks through two local models, differentiated by hardware. Our findings reveal a practical roadmap for achieving this transformation, detailing the necessary infrastructure and performance benchmarks. Discover what it *actually* takes to bring AI assistance home. For further insights on optimizing AI workflows, explore our analysis of Polars versus Pandas.
Stop Calling the First Significant Day a Win
Prematurely declaring an A/B test "won" based on the first statistically significant result is a common, and ultimately flawed, practice. Instead, rigorous testing demands continued monitoring – even after initial success. This approach ensures the observed improvement isn't a statistical anomaly and validates long-term performance. Short-term wins can be misleading; sustained data validation is key. For a deeper dive into AI’s capabilities in tackling complex challenges, explore "An unreleased Anthropic model made progress on one of math’s biggest unsolved problems."
Should AI Developers Make the Switch from Polars to Pandas?
Not all Python data libraries offer equal performance for AI development. Polars and Pandas are both popular choices, but their architectures differ significantly. This post explores whether AI developers should consider transitioning from Pandas to Polars, particularly given Polars’ optimized query engine and memory efficiency. Discover how these factors impact speed and scalability in modern data workflows. For deeper insights into agentic AI applications, see our recent article, "We built the Agentic World Cup - LLMs that compete in 1v1 Soccer [P]."
The Budget Split That Explains Itself
Traditional budget diversification often obscures the critical shadow prices that illuminate the underlying drivers of your financial result. Our latest approach, “The Budget Split That Explains Itself,” empowers you to explore diversified scenarios *without* sacrificing this essential interpretability. Discover a method for maintaining clarity and control, ensuring you understand *why* your budget performs as it does. For those seeking further insights into rigorous statistical validation, consider “Stop Calling the First Significant Day a Win,” which addresses critical considerations in A/B testing.
CIKM '26 Notification [D]
The results are in for CIKM '26! We're pleased to announce acceptances from our submissions, with 3 out of 6 full papers and 1 out of 3 short papers moving forward. A strong showing reflecting the innovative work being done in the field. For those seeking further context on related trends, consider exploring our piece, "2026 NeurIPS: Where are you going?" – a timely look at conference planning. Congratulations to all submitters and we look forward to seeing these contributions come to life.
Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick
Delve into Variational Autoencoders (VAEs), a powerful generative modeling technique, with our comprehensive, math-first walkthrough. This post systematically explores VAE theory, from the core concepts to the crucial Evidence Lower Bound (ELBO) and the reparameterization trick—essential for enabling efficient training. Understand how VAEs learn to generate new data by mastering these key components. For those seeking to build robust data infrastructure for AI agents, consider our related article, "Building an Agent-Ready Data Warehouse," which highlights common architectural pitfalls.