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#General #Development #Mobile Development #Cloud & DevOps #Cybersecurity & Privacy #AI & Data Science #Hardware & Gadgets #Design & UXVibing With Amazon Kiro
Learn how to implement leverage Amazon's agentic AI in your IDE.
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7 Pandas Tricks to Improve Your Machine Learning Model Development
If you're reading this, it's likely that you are already aware that the performance of a machine learning model is not just a function of the chosen algorithm.
Everything You Need to Know About the New Power BI Storage Mode
50 Shades of Direct Lake The post Everything You Need to Know About the New Power BI Storage Mode appeared first on Towards Data Science.
AI Agents for Supply Chain Optimisation: Production Planning
How to integrate an optimisation algorithm in a FastAPI microservice and connect it with an AI workflow to automate production planning. The post AI Agents for Supply Chain Optimisation: Production Planning appeared first on Towards Data Science.
My Most Valuable Lesson as an Aspiring Data Analyst
What my internship taught me about the power of collaboration in data analysis. The post My Most Valuable Lesson as an Aspiring Data Analyst appeared first on Towards Data Science.
Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance
Automating model tuning in Python with Gemini, LangGraph, and Streamlit for regression and classification improvements The post Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance appeared first on Towards Data Science.
βWhereβs Marta?β: How We Removed Uncertainty From AI Reasoning
A primer on overcoming LLM limitations with formal verification. The post βWhereβs Marta?β: How We Removed Uncertainty From AI Reasoning appeared first on Towards Data Science.
The Upstream Mentality: Why AI/ML Engineers Must Think Beyond the Model
Your 3am production alert isn't a model problemβit's an upstream crisis in disguise The post The Upstream Mentality: Why AI/ML Engineers Must Think Beyond the Model appeared first on Towards Data Science.
Debugging Python in Docker: A Tutorial for Beginners
New to running Python in Docker? This step-by-step guide helps you understand and apply debugging techniques in a containerized environment.
A Practical Guide to Handling Out-of-Memory Data in Python
These days, it is not uncommon to come across datasets that are too large to fit into random access memory (RAM), especially when working on advanced data analysis projects at scale, managing streaming data generated at high velocity, or building large machine learning models.
Leveraging Pandas and SQL Together for Efficient Data Analysis
Learn to leverage Pandas and SQL together while solving a real-world Uber data project.
Building a Modern Dashboard with Python and Tkinter
Create polished GUIs and data dashboards with this versatile library The post Building a Modern Dashboard with Python and Tkinter appeared first on Towards Data Science.
Mastering NLP with spaCy β Part 3
Rule-based matching for information extraction The post Mastering NLP with spaCy β Part 3 appeared first on Towards Data Science.
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.
Advanced Prompt Engineering for Data Science Projects
Part 2: Prompt Engineering for Features, Modeling, and Evaluation The post Advanced Prompt Engineering for Data Science Projects appeared first on Towards Data Science.
Water Cooler Small Talk, Ep 8: Should ChatGPT Be Blocked at Work?
Water cooler small talk is a special kind of small talk, typically observed in office spaces around a water cooler. There, employees frequently share all kinds of corporate gossip, myths, legends, inaccurate scientific opinions, indiscreet personal anecdotes, or outright lies. Anything goes. So, in my Water Cooler Small Talk posts, I discuss strange and usually [β¦] The post Water Cooler Small Talk, Ep 8: Should ChatGPT...
Water Cooler Small Talk: Should ChatGPT Be Blocked at Work?
Water cooler small talk is a special kind of small talk, typically observed in office spaces around a water cooler. There, employees frequently share all kinds of corporate gossip, myths, legends, inaccurate scientific opinions, indiscreet personal anecdotes, or outright lies. Anything goes. So, in my Water Cooler Small Talk posts, I discuss strange and usually [β¦] The post Water Cooler Small Talk: Should ChatGPT Be...
Getting Started with Couchbase: Installation and Setup Guide
This article explains how to install and setup Couchbase and start easily storing data.
The Bias-Variance Trade-Off: A Visual Explainer
You've built a machine learning model that performs perfectly on training data but fails on new examples.
7 Surprisingly Useful Python Scripts Youβll Use Every Week
Not using Python for daily life? You're missing out on the best cheat codes for productivity.
Can LangExtract Turn Messy Clinical Notes into Structured Data?
Turning raw clinical notes into structured entities withLLMs. The post Can LangExtract Turn Messy Clinical Notes into Structured Data? appeared first on Towards Data Science.
Modular Arithmetic in Data Science
Modular arithmetic is a mathematical system where numbers cycle back to the beginning after reaching a value called the modulus. The system is often referred to as βclock arithmeticβ due to its similarity to how analog 12-hour clocks represent time. This article provides a conceptual overview of modular arithmetic and explores practical use cases in [β¦] The post Modular Arithmetic in Data Science appeared first on...
Maximizing AI/ML Model Performance with PyTorch Compilation
Since its inception inPyTorch 2.0in March 2023, the evolution of torch.compile has been one of the most exciting things to follow. Given that PyTorchβs popularity was due to its βPythonicβ nature, its ease of use, and its line-by-line (a.k.a., eager) execution, the success of a just-in-time (JIT) graph compilation mode should not have been taken [β¦] The post Maximizing AI/ML Model Performance with PyTorch Compilation...
How to Correctly Apply Limits on the Result in DAX (and SQL)
What if the output of a measure mustnβt be above a specific limit? How can we ensure that the total is calculated correctly? This piece is about correctly calculating and summarizing such output. The post How to Correctly Apply Limits on the Result in DAX (and SQL) appeared first on Towards Data Science.
How to Create Powerful LLM Applications with Context Engineering
Improve your LLM by optimizing its context The post How to Create Powerful LLM Applications with Context Engineering appeared first on Towards Data Science.
Writing Your First GPU Kernel in Python with Numba and CUDA
80x Faster Python? Discover How One Line Turns Your Code Into a GPU Beast!
If Youβre Trying to Get Into AI, This Is What You Need to Do
Apply these 3 important lessons from the top minds in AI for your own professional success.
How to Diagnose Why Your Classification Model Fails
In classification models , failure occurs when the model assigns the wrong class to a new data observation; that is, when its classification accuracy is not high enough over a certain number of predictions.
7 NumPy Tricks You Didnβt Know You Needed
NumPy is one of the most popular Python libraries for working with numbers and data.
5 Lesser-Known Python Features Every Data Scientist Should Know
Learn about five handy Python features that many people miss but can make your data science work easier.
Stress Testing FastAPI Application
Build an optimized asynchronous machine learning application, then use Locust to stress test your app and determine if it is production-ready.
How I Use AI Agents as a Data Scientist in 2025
And why data scientists must master AI agents before manual analysis becomes obsolete.
What Does βFollowing Best Practicesβ Mean in the Age of AI?
How data and ML practitioners should navigate a rapidly changing landscape The post What Does βFollowing Best Practicesβ Mean in the Age of AI? appeared first on Towards Data Science.
Introducing Gemma 3 270M: The compact model for hyper-efficient AI
Today, we're adding a new, highly specialized tool to the Gemma 3 toolkit: Gemma 3 270M, a compact, 270-million parameter model.
7 Matplotlib Tricks to Better Visualize Your Machine Learning Models
Visualizing model performance is an essential piece of the machine learning workflow puzzle.