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Tag: your

  • Context Engineering as Your Competitive Edge

    Context Engineering as Your Competitive Edge If you have both unique domain expertise and know how to make it usable to your AI systems, you’ll be hard to beat. The post Context Engineering as Your Competitive Edge appeared first on Towards Data Science. Dr. Janna Lipenkova 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…

  • Your First 90 Days as a Data Scientist

    Your First 90 Days as a Data Scientist A practical onboarding checklist for building trust, business fluency, and data intuition The post Your First 90 Days as a Data Scientist appeared first on Towards Data Science. Yu Dong Go to original source

  • Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes

    Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes How much of your AI agent’s output is real data versus confident guesswork? The post Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes appeared first on Towards Data Science. James Barney Go to original source

  • Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents”

    Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy of core agent types. The post Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” appeared first…

  • You Probably Don’t Need a Vector Database for Your RAG — Yet

    You Probably Don’t Need a Vector Database for Your RAG — Yet Numpy or SciKit-Learn might meet all your retrieval needs The post You Probably Don’t Need a Vector Database for Your RAG — Yet appeared first on Towards Data Science. Thomas Reid Go to original source

  • How to Optimize Your AI Coding Agent Context

    How to Optimize Your AI Coding Agent Context Make your coding agents more efficient The post How to Optimize Your AI Coding Agent Context appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Think Your Python Code Is Slow? Stop Guessing and Start Measuring

    Think Your Python Code Is Slow? Stop Guessing and Start Measuring A hands-on tour of using cProfile + SnakeViz to find (and fix) the “hot” paths in your code. The post Think Your Python Code Is Slow? Stop Guessing and Start Measuring appeared first on Towards Data Science. Thomas Reid Go to original source

  • Is Your Model Time-Blind? The Case for Cyclical Feature Encoding

    Is Your Model Time-Blind? The Case for Cyclical Feature Encoding How cyclical encoding improves machine learning prediction The post Is Your Model Time-Blind? The Case for Cyclical Feature Encoding appeared first on Towards Data Science. Gustavo Santos Go to original source

  • How to Turn Your LLM Prototype into a Production-Ready System

    How to Turn Your LLM Prototype into a Production-Ready System The most famous applications of LLMs are the ones that I like to call the “wow effect LLMs.” There are plenty of viral LinkedIn posts about them, and they all sound like this: “I built [x] that does [y] in [z] minutes using AI.” Where:…

  • Metric Deception: When Your Best KPIs Hide Your Worst Failures

    Metric Deception: When Your Best KPIs Hide Your Worst Failures The most dangerous KPIs aren’t broken; they’re the ones trusted long after they’ve lost their meaning. The post Metric Deception: When Your Best KPIs Hide Your Worst Failures appeared first on Towards Data Science. Shafeeq Ur Rahaman Go to original source

  • Your Next ‘Large’ Language Model Might Not Be Large After All

    Your Next ‘Large’ Language Model Might Not Be Large After All A 27M-parameter model just outperformed giants like DeepSeek R1, o3-mini, and Claude 3.7 on reasoning tasks The post Your Next ‘Large’ Language Model Might Not Be Large After All appeared first on Towards Data Science. Moulik Gupta 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…

  • How to Build Your Own Agentic AI System Using CrewAI

    How to Build Your Own Agentic AI System Using CrewAI This article demonstrates how to develop your own Agentic AI system using CrewAI framework. By orchestrating specialized agents with distinct roles and tools, we implement a multi-agent team that is capable of generating optimized content for different social media platforms. The post How to Build…

  • Let Hypothesis Break Your Python Code Before Your Users Do

    Let Hypothesis Break Your Python Code Before Your Users Do Property-based tests that find bugs you didn’t know existed. The post Let Hypothesis Break Your Python Code Before Your Users Do appeared first on Towards Data Science. Thomas Reid Go to original source

  • 4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance

    4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance Learn how to greatly improve the performance of your LLM application The post 4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Know Your Real Birthday: Astronomical Computation and Geospatial-Temporal Analytics in Python

    Know Your Real Birthday: Astronomical Computation and Geospatial-Temporal Analytics in Python A hands-on walkthrough using skyfield, timezonefinder, geopy, and pytz, and further practical applications The post Know Your Real Birthday: Astronomical Computation and Geospatial-Temporal Analytics in Python appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Why am I not getting responses?

    Why am I not getting responses? As mentioned before, I can’t use the weekly transition because it doesn’t allow pictures. I appreciate your help last time when I asked. I’ve implemented your recommendations but I’m still not getting responses. I’ve added a completely new ML-based project, fixed mistakes, revamped the layout and I’m still not…

  • Evaluating Your RAG Solution

    Evaluating Your RAG Solution A guide to building and evaluating RAG solutions by leveraging LLM-as-a-Judge capabilities. The post Evaluating Your RAG Solution appeared first on Towards Data Science. Alex Davis Go to original source

  • Why Your A/B Test Winner Might Just Be Random Noise

    Why Your A/B Test Winner Might Just Be Random Noise What a coach’s warm-up trial can teach us about running better experiments The post Why Your A/B Test Winner Might Just Be Random Noise appeared first on Towards Data Science. Pol Marin Go to original source

  • How to Analyze and Optimize Your LLMs in 3 Steps

    How to Analyze and Optimize Your LLMs in 3 Steps Learn to enhance your LLMs with my 3 step process, inspecting, improving and iterating on your LLMs The post How to Analyze and Optimize Your LLMs in 3 Steps appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Is Your Training Data Representative? A Guide to Checking with PSI in Python

    Is Your Training Data Representative? A Guide to Checking with PSI in Python Comparing Variable Distributions Between Two Datasets Using Population Stability Index (PSI) and Cramér’s V. The post Is Your Training Data Representative? A Guide to Checking with PSI in Python appeared first on Towards Data Science. JUNIOR JUMBONG Go to original source

  • LangGraph 201: Adding Human Oversight to Your Deep Research Agent

    LangGraph 201: Adding Human Oversight to Your Deep Research Agent Losing control of your AI agent in the middle of the workflow is a common pain point. If you have built your own agentic applications, you’ve most likely already seen this happen. While LLMs nowadays are incredibly capable, they’re still not quite there yet to…

  • Showcasing Your Work on HuggingFace Spaces

    Showcasing Your Work on HuggingFace Spaces Building an app is exciting – but sharing it is where the real value kicks in. Back when Heroku offered a free tier, deploying demos was effortless. Those days are gone, and finding a simple, free way to showcase machine learning apps has become harder. That’s where Hugging Face…

  • Boosting Your Anomaly Detection With LLMs

    Boosting Your Anomaly Detection With LLMs The 7 emerging application patterns you should know The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science. Shuai Guo Go to original source

  • How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques

    How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques Optimize your AI search with RAG, contextual retrieval and evaluations The post How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Why Your Prompts Don’t Belong in Git

    Why Your Prompts Don’t Belong in Git The hidden cost of storing prompts in your source code The post Why Your Prompts Don’t Belong in Git appeared first on Towards Data Science. Giorgos Myrianthous Go to original source

  • Help Your Model Learn the True Signal

    Help Your Model Learn the True Signal An algorithm-agnostic approach inspired by Cook’s distance The post Help Your Model Learn the True Signal appeared first on Towards Data Science. Mena Wang Go to original source

  • AI isn’t taking your job. Executives are.

    AI isn’t taking your job. Executives are. If AI is ready to replace developers, why aren’t developers replacing themselves with AI and just taking it easy at work? I’m a Director at my company. I’m in the meetings and helping set up the tools that cost people their jobs. Here’s how they work: Claude AI…

  • How Your Prompts Lead AI Astray

    How Your Prompts Lead AI Astray Practical tips to recognise and avoid prompt bias. The post How Your Prompts Lead AI Astray appeared first on Towards Data Science. Daphne de Klerk Go to original source

  • Run Your Python Code up to 80x Faster Using the Cython Library

    Run Your Python Code up to 80x Faster Using the Cython Library A four-step plan for C language speed where it matters most The post Run Your Python Code up to 80x Faster Using the Cython Library appeared first on Towards Data Science. Thomas Reid Go to original source

  • Your Personal Analytics Toolbox

    Your Personal Analytics Toolbox Leveraging MCP for automating your daily routine The post Your Personal Analytics Toolbox appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Long-timers at companies — what’s your secret?

    Long-timers at companies — what’s your secret? Hi everyone, I’ve been a job hopper throughout my career—never stayed at one place for more than 1-2 years, usually for various reasons. Now, I’m entering a phase where I want to get more settled. I’m about to start a new job and would love to hear from…

  • [Project] I just open-sourced a plugin to stop AI from hallucinating your schemas

    [Project] I just open-sourced a plugin to stop AI from hallucinating your schemas Hey r/datascience 👋 Using AI tools like Copilot or Cursor can be a total headache for data science work. You’re trying to join tables, and it confidently suggests customer_id when your table actually uses cust_pk. Or worse, it just invents tables that…

  • Build an AI Agent to Explore Your Data Catalog with Natural Language

    Build an AI Agent to Explore Your Data Catalog with Natural Language Leverage LLMs to query your Databricks Data Catalog The post Build an AI Agent to Explore Your Data Catalog with Natural Language appeared first on Towards Data Science. Fabiana Clemente Go to original source

  • What is your domain and what are the most important technical skills that help you stand out in your domain?

    What is your domain and what are the most important technical skills that help you stand out in your domain? Aside from soft skills and domain expertise, ofc those are a given. I’m manufacturing-adjacent (closer to product development and validation). Design of experiments has been my most useful data-related skill. I’m always being asked “We…

  • Your DNA Is a Machine Learning Model: It’s Already Out There

    Your DNA Is a Machine Learning Model: It’s Already Out There Even if you never sequenced your genome, predictive systems already know a lot about it. Genomic inference has become a population-scale model, and you’re probably in it. The post Your DNA Is a Machine Learning Model: It’s Already Out There appeared first on Towards…

  • What is your functional area?

    What is your functional area? I don’t mean industry. I mean product, operations, etc. I work in operations. I don’t grow the business. I keep the business alive. submitted by /u/Trick-Interaction396 [link] [comments] /u/Trick-Interaction396 Go to original source

  • Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit

    Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit In this post, I’ll show you step by step how to build and deploy a chat powered with LLM — Gemini — in Streamlit and monitor the API usage on Google Cloud Console. Streamlit is a Python framework that makes it super easy to turn your…

  • A Step-By-Step Guide To Powering Your Application With LLMs

    A Step-By-Step Guide To Powering Your Application With LLMs You might be wondering whether GenAI is just hype or external noise. I also thought this was hype, and I could sit this one out until the dust cleared. Oh, boy, was I wrong. GenAI has real-world applications. It also generates revenue for companies, so we expect…

  • Load-Testing LLMs Using LLMPerf

    Load-Testing LLMs Using LLMPerf Deploying your Large Language Model (LLM) is not necessarily the final step in productionizing your Generative AI application. An often forgotten, yet crucial part of the MLOPs lifecycle is properly load testing your LLM and ensuring it is ready to withstand your expected production traffic. Load testing at a high level…

  • Learnings from a Machine Learning Engineer — Part 6: The Human Side

    Learnings from a Machine Learning Engineer — Part 6: The Human Side In my previous articles, I have spent a lot of time talking about the technical aspects of an Image Classification problem from data collection, model evaluation, performance optimization, and a detailed look at model training. These elements require a certain degree of in-depth expertise, and they (usually) have well-defined…

  • Avoiding Costly Mistakes with Uncertainty Quantification for Algorithmic Home Valuations

    Avoiding Costly Mistakes with Uncertainty Quantification for Algorithmic Home Valuations When you’re about to buy a home, whether you’re an everyday buyer looking for your dream house or a seasoned property investor, there’s a good chance you’ve encountered automated valuation models, or AVMs. These clever tools use massive datasets filled with past property transactions to…

  • Kubernetes — Understanding and Utilizing Probes Effectively

    Kubernetes — Understanding and Utilizing Probes Effectively Introduction Let’s talk about Kubernetes probes and why they matter in your deployments. When managing production-facing containerized applications, even small optimizations can have enormous benefits. Aiming to reduce deployment times, making your applications better react to scaling events, and managing the running pods healthiness requires fine-tuning your container…

  • Mastering 1:1s as a Data Scientist: From Status Updates to Career Growth

    Mastering 1:1s as a Data Scientist: From Status Updates to Career Growth I have been a data team manager for six months, and my team has grown from three to five. I wrote about my initial manager experiences back in November. In this article, I want to talk about something that is more essential to…

  • Write for Towards Data Science

    Write for Towards Data Science Quick Links: Submission Guidelines How To Submit Your Work How to get your article ready for publication! Adding and using images Longform posts, columns, and online books FAQ Why become a contributor? We are looking for writers to propose up-to-date content focused on data science, machine learning, artificial intelligence and…

  • Nine Rules for SIMD Acceleration of Your Rust Code (Part 1)

    Nine Rules for SIMD Acceleration of Your Rust Code (Part 1) Thanks to Ben Lichtman (B3NNY) at the Seattle Rust Meetup for pointing me in the right direction on SIMD. SIMD (Single Instruction, Multiple Data) operations have been a feature of Intel/AMD and ARM CPUs since the early 2000s. These operations enable you to, for example,…

  • ➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality

    ➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. This post is targeted towards anyone making data driven…

  • Learnings from a Machine Learning Engineer — Part 5: The Training

    Learnings from a Machine Learning Engineer — Part 5: The Training In this fifth part of my series, I will outline the steps for creating a Docker container for training your image classification model, evaluating performance, and preparing for deployment. AI/ML engineers would prefer to focus on model training and data engineering, but the reality…

  • Manage Environment Variables with Pydantic

    Manage Environment Variables with Pydantic Introduction Developers work on applications that are supposed to be deployed on some server in order to allow anyone to use those. Typically in the machine where these apps live, developers set up environment variables that allow the app to run. These variables can be API keys of external services,…

  • How to Get Promoted as a Data Scientist

    How to Get Promoted as a Data Scientist Image artificially generated using Grok 2. Introduction I have been working as a Data Scientist since 2017, and during that time I have been promoted from a junior/mid-level to a senior, and most recently to a Lead Data Scientist. There is a lot of content online regarding…

  • Inequality in Practice: E-commerce Portfolio Analysis

    Inequality in Practice: E-commerce Portfolio Analysis From Mathematical Theory to Actionable Insights: A 6-Year Shopify Case Study Image generated by DALL-E, based on author’s prompt, inspired by “The Bremen Town Musicians” Are your top-selling products making or breaking your business? It’s terrifying to think your entire revenue might collapse if one or two products fall out…

  • Stop Creating Bad DAGs — Optimize Your Airflow Environment By Improving Your Python Code

    Stop Creating Bad DAGs — Optimize Your Airflow Environment By Improving Your Python Code Stop Creating Bad DAGs — Optimize Your Airflow Environment By Improving Your Python Code Valuable tips to reduce your DAGs’ parse time and save resources. Photo by Dan Roizer on Unsplash Apache Airflow is one of the most popular orchestration tools in the data field, powering workflows…

  • How to Implement Guardrails for Your AI Agents with CrewAI

    How to Implement Guardrails for Your AI Agents with CrewAI LLM Agents are non-deterministic by nature: implement proper guardrails for your AI Application. Continue reading on Towards Data Science » Alessandro Romano Go to original source

  • Your Neural Network Can’t Explain This. TMLE to the Rescue!

    Your Neural Network Can’t Explain This. TMLE to the Rescue! Targeted Maximum Likelihood Estimation (TMLE) helps you explain patterns where other techniques fall short Continue reading on Towards Data Science » Ari Joury, PhD Go to original source

  • Where to Start When Data is Limited

    Where to Start When Data is Limited A launch pad for projects with small datasets Photo by Google DeepMind: https://www.pexels.com/photo/an-artist-s-illustration-of-artificial-intelligence-ai-this-image-depicts-how-ai-can-help-humans-to-understand-the-complexity-of-biology-it-was-created-by-artist-khyati-trehan-as-part-17484975/ Machine Learning (ML) has driven remarkable breakthroughs in computer vision, natural language processing, and speech recognition, largely due to the abundance of data in these fields. However, many challenges — especially those tied to specific product features or…

  • LangChain Meets Home Assistant: Unlock the Power of Generative AI in Your Smart Home

    LangChain Meets Home Assistant: Unlock the Power of Generative AI in Your Smart Home Learn how to create an agent that understands your home’s context, learns your preferences, and interacts with you and your home to accomplish activities you find valuable. Photo by Igor Omilaev on Unsplash Introduction This article describes the architecture and design of…

  • How to Clean Your Data for Your Real-Life Data Science Projects

    How to Clean Your Data for Your Real-Life Data Science Projects How I treat missing values—with a quick Python Guide Continue reading on Towards Data Science » Mythili Krishnan Go to original source

  • Will Your Christmas Be White? Ask An AI Weather Model!

    Will Your Christmas Be White? Ask An AI Weather Model! Learn how to visualize AI weather and create your own forecast for the holidays Continue reading on Towards Data Science » Caroline Arnold Go to original source

  • What projects are you working on and what is the benefit of your efforts?

    What projects are you working on and what is the benefit of your efforts? I would really like to hear what you guys are working on, challenges you’re facing and how your project is helping your company. Let’s hear it. submitted by /u/Firm-Message-2971 [link] [comments] /u/Firm-Message-2971 Go to original source

  • 3 Business Skills You Need to Progress Your Data Science Career in 2025

    3 Business Skills You Need to Progress Your Data Science Career in 2025 DATA SCIENCE Including resources for how to build those skills Image by Author. Created using Midjourney If you have been a data scientist for a while, sooner or later you’ll notice that your day-to-day has shifted from a VSCode-loving, research paper-reading, git-version-committing data…

  • Streamline Your Workflow when Starting a New Research Paper

    Streamline Your Workflow when Starting a New Research Paper Python code to create folders and Word documents for research papers in biomedical sciences — all in one go with only two inputs Continue reading on Towards Data Science » Rodrigo M Carrillo Larco, MD, PhD Go to original source

  • How to Prepare for Your Data Science Behavioural Interview

    How to Prepare for Your Data Science Behavioural Interview My top tips to smash your next data science behavioural interview Continue reading on Towards Data Science » Egor Howell Go to original source

  • run your own pg rds

    https://pigsty.cc/docs/about/feature/

  • The Intuition behind Concordance Index — Survival Analysis

    The Intuition behind Concordance Index — Survival Analysis The Intuition behind Concordance Index — Survival Analysis Ranking accuracy versus absolute accuracy Taken by the author and her Border Collie. “Be thankful for what you have. Be fearless for what you want” How long would you keep your Gym membership before you decide to cancel it? or Netflix if you are a series…

  • Level Up Your Coding Skills with Python Threading

    Level Up Your Coding Skills with Python Threading Learn how to use queues, daemon threads, and events in a Machine Learning project Continue reading on Towards Data Science » Marcello Politi Go to original source