mailitics

Category: ai

  • Architecting GPUaaS for Enterprise AI On-Prem

    Architecting GPUaaS for Enterprise AI On-Prem Multi-tenancy, scheduling, and cost modeling on Kubernetes The post Architecting GPUaaS for Enterprise AI On-Prem appeared first on Towards Data Science. Joe Sasson Go to original source

  • Can AI Solve Failures in Your Supply Chain?

    Can AI Solve Failures in Your Supply Chain? When your warehouse and transportation teams blame each other for late deliveries, who’s right? We can ask an agent connected to the data settle the debate. The post Can AI Solve Failures in Your Supply Chain? appeared first on Towards Data Science. Samir Saci Go to original…

  • The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies

    The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies How to use n8n with multimodal AI and optimisation tools to help companies with low data maturity accelerate their digital transformation. The post The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies appeared first on Towards Data Science. Samir Saci…

  • How AI Can Become Your Personal Language Tutor

    How AI Can Become Your Personal Language Tutor How I used n8n to build AI study partners for learning Mandarin: vocabulary, listening, and pronunciation correction. The post How AI Can Become Your Personal Language Tutor appeared first on Towards Data Science. Samir Saci Go to original source

  • Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP

    Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP Use Claude AI to monitor, analyse, and troubleshoot your n8n automation workflows through natural conversation. The post Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP appeared first on Towards Data Science. Samir Saci Go to…

  • Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs

    Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs Understanding how AI models “reason” and why it’s not what humans do when we think The post Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs appeared first on Towards Data Science.…

  • STAT+: FDA will convene Digital Health Advisory Committee in November

    STAT+: FDA will convene Digital Health Advisory Committee in November The Food and Drug Administration will convene a meeting of external digital health advisors on Nov. 6, according to an email reviewed by STAT. The gathering comes as the agency wrestles with myriad issues related to regulating artificial intelligence and other technologies in health care.…

  • Designing Trustworthy ML Models: Alan & Aida Discover Monotonicity in Machine Learning

    Designing Trustworthy ML Models: Alan & Aida Discover Monotonicity in Machine Learning Accuracy alone doesn’t guarantee trustworthiness. Monotonicity ensures predictions align with common sense and business rules. The post Designing Trustworthy ML Models: Alan & Aida Discover Monotonicity in Machine Learning appeared first on Towards Data Science. Mehdi Mohammadi Go to original source

  • Work Data Is the Next Frontier for GenAI

    Work Data Is the Next Frontier for GenAI 9 reasons why work data is the single most valuable data source for LLM training, uniquely capable of propelling LLM performance to unprecedented heights. The post Work Data Is the Next Frontier for GenAI appeared first on Towards Data Science. Zsombor Varnagy-Toth Go to original source

  • Stop Building AI Platforms

    Stop Building AI Platforms When small and medium companies achieve success in building Data and ML platforms, building AI platforms is now profoundly challenging The post Stop Building AI Platforms appeared first on Towards Data Science. Ming Gao Go to original source

  • How to Build an AI Journal with LlamaIndex

    How to Build an AI Journal with LlamaIndex This post will share how to build an AI journal with the LlamaIndex. We will cover one essential function of this AI journal: asking for advice. We will start with the most basic implementation and iterate from there. We can see significant improvements for this function when…

  • Former FDA head Robert Califf says RFK Jr.’s vaccine rhetoric is ‘doing harm’ to Americans

    Former FDA head Robert Califf says RFK Jr.’s vaccine rhetoric is ‘doing harm’ to Americans SAN FRANCISCO — Former Food and Drug Administration Commissioner Robert Califf said health secretary Robert F. Kennedy Jr.’s hesitance to endorse vaccines is harming the American public.  “Fundamental vaccination saves millions of lives every year already, and he’s busy eroding public…

  • STAT+: Insitro, an AI biotech founded by Daphne Koller, lays off staff

    STAT+: Insitro, an AI biotech founded by Daphne Koller, lays off staff Insitro, the closely watched AI biotech founded by the Stanford professor and entrepreneur Daphne Koller, said Thursday it was laying off 22% of its staff, or around 60 people.  It’s part of a series of cuts that have hit well-backed startups over the…

  • Circuit Tracing: A Step Closer to Understanding Large Language Models

    Circuit Tracing: A Step Closer to Understanding Large Language Models Context Over the years, Transformer-based large language models (LLMs) have made substantial progress across a wide range of tasks evolving from simple information retrieval systems to sophisticated agents capable of coding, writing, conducting research, and much more. But despite their capabilities, these models are still largely…

  • How I Became A Machine Learning Engineer (No CS Degree, No Bootcamp)

    How I Became A Machine Learning Engineer (No CS Degree, No Bootcamp) Machine learning and AI are among the most popular topics nowadays, especially within the tech space. I am fortunate enough to work and develop with these technologies every day as a machine learning engineer! In this article, I will walk you through my…

  • I Tried Making my Own (Bad) LLM Benchmark to Cheat in Escape Rooms

    I Tried Making my Own (Bad) LLM Benchmark to Cheat in Escape Rooms Recently, DeepSeek announced their latest model, R1, and article after article came out praising its performance relative to cost, and how the release of such open-source models could genuinely change the course of LLMs forever. That is really exciting! And also, too…

  • Neural Networks – Intuitively and Exhaustively Explained

    Neural Networks – Intuitively and Exhaustively Explained An in-depth exploration of the most fundamental architecture in modern AI “The Thinking Part” by Daniel Warfield using MidJourney. All images by the author unless otherwise specified. Article originally made available on Intuitively and Exhaustively Explained. In this article we’ll form a thorough understanding of the neural network,…

  • The Cultural Backlash Against Generative AI

    The Cultural Backlash Against Generative AI What’s making many people resent generative AI, and what impact does that have on the companies responsible? Photo by Joshua Hoehne on Unsplash The recent reveal of DeepSeek-R1, the large scale LLM developed by a Chinese company (also named DeepSeek), has been a very interesting event for those of us…

  • Rapid Data Visualization with Copilot and Plotly

    Rapid Data Visualization with Copilot and Plotly Code visualizations quickly and efficiently with Copilot, Plotly, and Streamlit Continue reading on Towards Data Science » Alan Jones Go to original source

  • Fine-tuning Multimodal Embedding Models

    Fine-tuning Multimodal Embedding Models Adapting CLIP to YouTube Data (with Python Code) This is the 4th article in a larger series on multimodal AI. In the previous post, we discussed multimodal RAG systems, which can retrieve and synthesize information from different data modalities (e.g. text, images, audio). There, we saw how we could implement such a…

  • Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data

    Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data How much data does AI really need? TLDR: Data-centric AI can create more efficient and accurate models. I experimented with data pruning on MNIST¹ to classify handwritten digits. Best runs for “furthest-from-centroid” selection compared to full dataset. Image by author. What if I told you…

  • Great Books for AI Engineering

    Great Books for AI Engineering 10 books with valuable insights about AI science and engineering Great books for AI Engineering — Plus ‘Brave New Words’ (Image is Author’s own work) A few years ago I recommended 21 books in Great Books for Data Science and Great Books for Data Science 2. Since then a lot has changed. While…

  • Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems

    Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems How natural systems fundamental laws help explain AI’s unexpected abilities Continue reading on Towards Data Science » Javier Marin Go to original source

  • How to Utilize ModernBERT and Synthetic Data for Robust Text Classification

    How to Utilize ModernBERT and Synthetic Data for Robust Text Classification Learn how to fine-tune ModernBERT and create augmentations of text samples Continue reading on Towards Data Science » Eivind Kjosbakken Go to original source

  • Large Language Models: A Short Introduction

    Large Language Models: A Short Introduction And why you should care about LLMs Image by author. There’s an acronym you’ve probably heard non-stop for the past few years: LLM, which stands for Large Language Model. In this article we’re going to take a brief look at what LLMs are, why they’re an extremely exciting piece of technology, why…

  • Why Generative-AI Apps’ Quality Often Sucks and What to Do About It

    Why Generative-AI Apps’ Quality Often Sucks and What to Do About It How to get from PoCs to tested high-quality applications in production Image licensed from elements.envato.com, edit by Marcel Müller, 2025 The generative AI hype has rolled through the business world in the past two years. This technology can make business process executions more efficient,…

  • How to Log Your Data with MLflow

    How to Log Your Data with MLflow MLflow, MLOps, Data Science Mastering data logging in MLOps for your AI workflow Photo by Chris Liverani on Unsplash Preface Data is one of the most critical components of the machine learning process. In fact, the quality of the data used in training a model often determines the success or failure…

  • Machine Learning + openAI: solving a text classification problem

    Machine Learning + openAI: solving a text classification problem How I migrated an old solution to a more elegant, robust and scalable solution using text classification from openAI Continue reading on Towards Data Science » Ricardo Ribas Go to original source

  • What to Do If the Logit Decision Boundary Fails?

    What to Do If the Logit Decision Boundary Fails? Feature engineering for classification models using Bayesian Machine Learning Continue reading on Towards Data Science » Lukasz Gatarek Go to original source

  • AI Agents Hype, Explained — What You Really Need to Know to Get Started

    AI Agents Hype, Explained — What You Really Need to Know to Get Started I’ll set the record straight — AI Agents are not new but advanced. Learn how they’ve evolved and where to get started. Continue reading on Towards Data Science » Marc Nehme Go to original source

  • The Cultural Impact of AI Generated Content: Part 2

    The Cultural Impact of AI Generated Content: Part 2 What can we do about the increasingly sophisticated AI generated content in our lives? Photo by Meszárcsek Gergely on Unsplash In my prior column, I established how AI generated content is expanding online, and described scenarios to illustrate why it’s occurring. (Please read that before you go on…

  • GDD: Generative Driven Design

    GDD: Generative Driven Design Reflective generative AI software components as a development paradigm Nowhere has the proliferation of generative AI tooling been more aggressive than in the world of software development. It began with GitHub Copilot’s supercharged autocomplete, then exploded into direct code-along integrated tools like Aider and Cursor that allow software engineers to dictate…

  • Top 12 Skills Data Scientists Need to Succeed in 2025

    Top 12 Skills Data Scientists Need to Succeed in 2025 It’s (not) all about LLMs and AI tools Continue reading on Towards Data Science » Benjamin Bodner Go to original source

  • Probability Distributions: Poisson vs. Binomial Distribution

    Probability Distributions: Poisson vs. Binomial Distribution Using Soccer to Understand the Difference Between Poisson & Binomial: Probability for Data Science Series (3) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source

  • What Every Aspiring Machine Learning Engineer Must Know to Succeed

    What Every Aspiring Machine Learning Engineer Must Know to Succeed Your Guide to Avoiding Critical Errors with Machine Learning in Production Continue reading on Towards Data Science » Claudia Ng Go to original source

  • Should you switch from VSCode to Cursor?

    Should you switch from VSCode to Cursor? My experience using VSCode (GitHub Copilot) and Cursor (Claude 3.5 Sonnet) as a Data Scientist. Continue reading on Towards Data Science » Marc Matterson Go to original source

  • Top 3 Strategies to Search Your Data

    Top 3 Strategies to Search Your Data Strategies from traditional index seek to AI based semantic search that every software engineer should know! Continue reading on Towards Data Science » Shawn Shi Go to original source

  • Introduction to TensorFlow’s Functional API

    Introduction to TensorFlow’s Functional API Learn what the Functional API is, and how to build complex keras models using it Continue reading on Towards Data Science » Javier Martínez Ojeda Go to original source

  • The Invisible Bug That Broke My Automation: How OCR Changed The Game

    The Invisible Bug That Broke My Automation: How OCR Changed The Game The evolution of AI in test automation: from locators to generative AI (Part 3) Continue reading on Towards Data Science » Abdelkader HASSINE Go to original source

  • Data Valuation — A Concise Overview

    Data Valuation — A Concise Overview Understanding the Value of your Data: Challenges, Methods, and Applications ChatGPT and similar LLMs were trained on insane amounts of data. OpenAI and Co. scraped the internet, collecting books, articles, and social media posts to train their models. It’s easy to imagine that some of the texts (like scientific or news…

  • How I’d Learn AI in 2025 (If I Knew Nothing)

    How I’d Learn AI in 2025 (If I Knew Nothing) A 5-step roadmap for today’s landscape Today, more people than ever are trying to learn AI. Although there are countless free learning resources online, navigating this rapidly evolving landscape can be overwhelming (especially as a beginner). In this article, I discuss how I’d approach learning…

  • Why “AI Can’t Reason” Is a Bias

    Why “AI Can’t Reason” Is a Bias We humans are proud creatures Continue reading on Towards Data Science » Rafe Brena, Ph.D. Go to original source

  • How to Integrate AI and Data Science into Your Business Strategy

    How to Integrate AI and Data Science into Your Business Strategy DATA SCIENCE CONSULTING Insider consulting guide to conducting a successful 2-day executive workshop Image by author using Canva “Our industry does not respect tradition — it only respects innovation.” — Satya Nadella, CEO Microsoft, Letter to employees in 2014 While not all industries are as competitive and cutthroat as the…

  • Multimodal RAG: Process Any File Type with AI

    Multimodal RAG: Process Any File Type with AI A beginner-friendly guide with example (Python) code This is the third article in a larger series on multimodal AI. In the previous posts, we discussed multimodal LLMs and embedding models, respectively. In this article, we will combine these ideas to enable the development of multimodal RAG systems. I’ll…

  • How to Build a General-Purpose LLM Agent

    How to Build a General-Purpose LLM Agent A Step-by-Step Guide High-level Overview of an LLM Agent. (Image by author) Why build a general-purpose agent? Because it’s an excellent tool to prototype your use cases and lays the groundwork for designing your own custom agentic architecture. Before we dive in, let’s quickly introduce LLM agents. Feel free…

  • What Teaching AI Taught me About Data Skills & People

    What Teaching AI Taught me About Data Skills & People Three key lessons from my journey as a corporate AI educator Photo by Mikhail Nilov. As an AI Educator, my job was to equip corporate teams with the data & AI skills they needed to thrive. But looking back, I realized that I learned far more from…

  • Bird’s-Eye View of Linear Algebra: Left, Right Inverse => Injective, Surjective Maps

    Bird’s-Eye View of Linear Algebra: Left, Right Inverse => Injective, Surjective Maps If matrix multiplication isn’t commutative, then why don’t we have left and right inverses? Continue reading on Towards Data Science » Rohit Pandey Go to original source

  • The Cultural Impact of AI Generated Content: Part 1

    The Cultural Impact of AI Generated Content: Part 1 What happens when AI generated media becomes ubiquitous in our lives? How does this relate to what we’ve experienced before, and how does it change us? Photo by Annie Spratt on Unsplash This is the first part of a two part series I’m writing analyzing how people and…

  • Smaller is smarter

    Smaller is smarter Concerns about the environmental impacts of Large Language Models (LLMs) are growing. Although detailed information about the actual costs of LLMs can be difficult to find, let’s attempt to gather some facts to understand the scale. Generated with ChatGPT-4o Since comprehensive data on ChatGPT-4 is not readily available, we can consider Llama 3.1…

  • How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs?

    How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? Delve into an end-to-end Machine Learning project to improve the quality of the Open Food Facts database Image generated with Flux1 Open Food Facts’ purpose is to create the largest open-source food database in the world. To this day, it has collected over 3 millions products…

  • Why Internal Company Chatbots Fail and How to Use Generative AI in Enterprise with Impact

    Why Internal Company Chatbots Fail and How to Use Generative AI in Enterprise with Impact Start with the problem and not with the solution Background licensed from elements.envato.com, edit by Marcel Müller 2024 The most common disillusion that many organizations have is the following: They get excited about generative AI with ChatGPT or Microsoft Co-Pilot, read some…

  • How to Develop an Effective AI-Powered Legal Assistant

    How to Develop an Effective AI-Powered Legal Assistant Create a machine-learning-based search into legal decisions Continue reading on Towards Data Science » Eivind Kjosbakken Go to original source