Sat.Jun 28, 2025 - Fri.Jul 04, 2025

article thumbnail

10 GitHub Awesome Lists for Data Science

Flipboard

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 GitHub Awesome Lists for Data Science Most popular educational resource list on GitHub for Python, R, SQL, analytics, machine learning, datasets, and more.

article thumbnail

Reimagining Data Architecture for Agentic AI

Dataversity

As agentic AI and autonomous systems transform the enterprise landscape, organizations face a new imperative: Fundamentally reimagining data architecture is no longer optional; it’s required for AI success. Many enterprises are coming to the realization that traditional data architectures, which are built for structured data and deterministic workloads, are ill-equipped to support agentic AI’s demands […] The post Reimagining Data Architecture for Agentic AI appeared first on DATAVERSITY.

AI 87
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How Is AI Really Impacting Jobs In 2025?

Flipboard

We’ve all seen the predictions that AI will be hugely transformative for jobs and employment, but we’re just not quite sure how.

article thumbnail

Introducing the Databricks AI Governance Framework

databricks

Today, we’re introducing the Databricks AI Governance Framework (DAGF v1.0), a structured and practical approach to governing AI adoption across the enterprise.

AI 246
article thumbnail

Precision in Motion: Why Process Optimization Is the Future of Manufacturing

Speaker: Jason Chester, Director, Product Management

In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.

article thumbnail

I write type-safe generic data structures in C

Hacker News

I write type safe generic data structures in C using a technique that I haven’t seen elsewhere1. It uses unions to associate type information with a generic data structure, but we’ll get to that. My approach works for any type of data structure: maps, arrays, binary trees… but for this article I illustrate the ideas by implementing a basic linked list.

154
154
article thumbnail

Serve Machine Learning Models via REST APIs in Under 10 Minutes

KDnuggets

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Serve Machine Learning Models via REST APIs in Under 10 Minutes Stop leaving your models on your laptop.

More Trending

article thumbnail

Lessons Learned After 6.5 Years Of Machine Learning

Flipboard

Publish AI, ML & data-science insights to a global community of data professionals. Sign in Sign out Submit an Article Latest Editor’s Picks Deep Dives Newsletter Write For TDS Toggle Mobile Navigation LinkedIn X Toggle Search Search Machine Learning Lessons Learned After 6.5 Years Of Machine Learning Deep work, trends, data, and research Pascal Janetzky Jun 30, 2025 7 min read Share Photo by Anthony Tori When I started learning machine learning more than six years ago, the field was in the

article thumbnail

Preview of ODSC West 2025: Your Ultimate Track Guide

ODSC - Open Data Science

From October 28–30 in San Francisco, ODSC West 2025 returns with a robust lineup of 15 tracks aimed at helping professionals build practical skills and stay ahead of emerging trends in AI. Whether you’re a data scientist, ML engineer, AI architect, or decision‑maker, these tracks offer curated content that spans foundational theory, hands‑on implementation, and strategic insight.

article thumbnail

Alice's Adventures in a Differentiable Wonderland

Hacker News

Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what is called differentiable programming.

article thumbnail

Streaming data architecture

Dataconomy

Streaming data architecture is transforming how organizations manage and analyze their data in real-time. With the increasing need for timely insights, businesses are adopting this architecture to process continuous streams of information efficiently. This paradigm shift allows companies to enhance decision-making capabilities and improve operational agility.

article thumbnail

Airflow Best Practices for ETL/ELT Pipelines

Speaker: Kenten Danas, Senior Manager, Developer Relations

ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!

article thumbnail

AI-First Google Colab is All You Need

KDnuggets

Let's take a closer look at Google Colab's new AI features, and find out how you can use them to increase your daily data workflow productivity.

AI 327
article thumbnail

AI’s Bright Future: Insights from ODSC East 2025 Podcast Minisodes

ODSC - Open Data Science

ODSC East 2025 once again delivered a powerhouse of AI insights, featuring a unique podcast episode recorded live with short interviews from some of the brightest minds in AI today. Across these minisodes, speakers explored cutting-edge topics ranging from AI agents, small language models, and AI risk management, to synthetic data, causal AI, and even social media algorithms.

article thumbnail

Mixture of Experts Architecture in Transformer Models

Machine Learning Mastery

This post covers three main areas: • Why Mixture of Experts is Needed in Transformers • How Mixture of Experts Works • Implementation of MoE in Transformer Models The Mixture of Experts (MoE) concept was first introduced in 1991 by

article thumbnail

Real-time analytics

Dataconomy

Real-time analytics is transforming the way businesses interact with their data, enabling them to make informed decisions swiftly and effectively. By analyzing data as it streams into a system, organizations can gain instantaneous insights into operations, customer behavior, and more. This capability is essential in today’s fast-paced environment, where timely information can make all the difference in achieving a competitive edge.

article thumbnail

Whats New in Apache Airflow 3.0 –– And How Will It Reshape Your Data Workflows?

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

article thumbnail

3 production-ready models released by Arcee AI on Hugging Face

Julien Simon

In this video, I introduce and demonstrate three production-grade models that Arcee AI recently opened and released on Hugging Face. Arcee-SuperNova-v1 (70B) is a merged model built from multiple advanced training approaches. At its core is a distilled version of Llama-3.1–405B-Instruct into Llama-3.1–70B-Instruct, using our DistillKit to preserve instruction-following strengths while reducing size.

AI 52
article thumbnail

Unlocking the Power of Synthetic Data: Privacy, Performance, and the Future of AI

ODSC - Open Data Science

As artificial intelligence continues its rapid evolution, organizations across industries are wrestling with a familiar conundrum: how to access and leverage data while preserving privacy and complying with stringent regulations. Enter synthetic data  — a transformative approach to AI development that addresses these challenges head-on. At the forefront of this movement is Alexandra Ebert, Chief AI and Data Democratization Officer at MOSTLY AI, a global leader in privacy-preserving synthetic dat

AI 52
article thumbnail

AI for Scientific Search

Hacker News

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research.

AI 128
article thumbnail

Corrective RAG: How to Build Self-Correcting Retrieval-Augmented Generation

Towards AI

Last Updated on July 4, 2025 by Editorial Team Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Retrieval-Augmented Generation (RAG) has completely transformed how we build Large Language Model (LLM) applications. It gives LLMs the superpower to fetch external knowledge and generate context-rich answers. But here’s the problem →Traditional RAG is like a GPS that always trusts the first route it shows → even if there’s a traffic jam.

AI 103
article thumbnail

Agent Tooling: Connecting AI to Your Tools, Systems & Data

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

article thumbnail

Why Google coders just got new AI rules

Dataconomy

According to 9to5Google , Google issued company-wide AI coding guidance to its software engineers, detailing best practices for AI adoption in their work, following CEO Sundar Pichai’s April 2025 statement that over 30% of Google’s code is AI-generated. The guidance, formally released today, was preceded by an email to all software engineers on Monday, providing recommendations and best practices for integrating AI into their workflows.

AI 103
article thumbnail

Agentic AI Communication Protocols: The Backbone of Autonomous Multi-Agent Systems

Data Science Dojo

Agentic AI communication protocols are at the forefront of redefining intelligent automation. Unlike traditional AI, which often operates in isolation, agentic AI systems consist of multiple autonomous agents that interact, collaborate, and adapt to complex environments. These agents, whether orchestrating supply chains, powering smart homes, or automating enterprise workflows, must communicate seamlessly to achieve shared goals.

AI 195
article thumbnail

The Evolution of Caching Libraries in Go

Hacker News

Skip to content Otter The Evolution of Caching Libraries in Go Initializing search maypok86/otter Overview User guide API Performance Blog Otter maypok86/otter Overview Overview Ask a question User guide User guide v2 manual v2 manual Getting started Examples Features Features Eviction Deletion Loading Refresh Bulk operations Compute Statistics Persistence Extension Iteration v1 manual v1 manual Getting started Features Features Expiration policy Cost-based eviction Statistics API Performance Pe

Algorithm 150
article thumbnail

How to Monitor, Diagnose, and Solve Gradient Issues in Foundation Models

The MLOps Blog

TL;DR Vanishing or exploding gradients are common training instabilities observed in foundation models. Real-time gradient-norm monitoring using experiment trackers like neptune.ai enables early detection and mitigation. Implementing stabilization techniques such as gradient clipping and optimizing weight initialization and learning rate schedules improves the training convergence and stability.

ML 59
article thumbnail

How to Modernize Manufacturing Without Losing Control

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

article thumbnail

Spatial data

Dataconomy

Spatial data plays a crucial role in our understanding of the world, enriching various applications from urban planning to environmental monitoring. As technology advances, the way we collect, analyze, and utilize this information continues to transform, unlocking new insights into patterns and relationships that are often invisible at first glance.

article thumbnail

60 Python Interview Questions For Data Analyst

Analytics Vidhya

Python powers most data analytics workflows thanks to its readability, versatility, and rich ecosystem of libraries like Pandas, NumPy, Matplotlib, SciPy, and scikit-learn. Employers frequently assess candidates on their proficiency with Python’s core constructs, data manipulation, visualization, and algorithmic problem-solving. This article compiles 60 carefully crafted Python coding interview questions and answers categorized by Beginner, […] The post 60 Python Interview Questions

article thumbnail

A Gentle Introduction to Principal Component Analysis (PCA) in Python

Flipboard

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter A Gentle Introduction to Principal Component Analysis (PCA) in Python The most popular method for feature reduction and data compression, gently explained via implementation with Scikit-learn in Python.

Python 132
article thumbnail

What is Uncertainty Quantification in Machine Learning?

Pickl AI

Summary: Uncertainty quantification in machine learning systematically measures and communicates how confident we should be in model predictions. By identifying and managing errors, noise, and model limitations, UQ supports safer, more reliable decisions in fields like healthcare, engineering, and finance. It is essential for building trustworthy, interpretable, and robust AI systems.

article thumbnail

What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

article thumbnail

What is garbage in, garbage out (GIGO)?

Dataconomy

Garbage in, garbage out (GIGO) highlights a fundamental truth in data processing: the quality of the output is only as good as the quality of the input. This principle resonates across various domains, from software development to data analysis, and underscores the critical relationship between input and results. Ensuring reliable data is paramount, especially as organizations increasingly leverage data-driven decision-making.

article thumbnail

Batch Processing vs Mini-Batch Training in Deep Learning

Analytics Vidhya

Deep learning has revolutionised the AI field by allowing machines to grasp more in-depth information within our data. Deep learning has been able to do this by replicating how our brain functions through the logic of neuron synapses. One of the most critical aspects of training deep learning models is how we feed our data […] The post Batch Processing vs Mini-Batch Training in Deep Learning appeared first on Analytics Vidhya.

article thumbnail

How Model Context Protocol (MCP) Simplifies AI Workflows and Enhances Productivity

Flipboard

Skip to main content Skip to secondary menu Skip to primary sidebar Skip to footer Geeky Gadgets The Latest Technology News Home Top News AI Apple Android Technology Guides Gadgets Hardware Gaming Autos Deals About How Model Context Protocol (MCP) Simplifies AI Workflows and Enhances Productivity 7:39 am June 30, 2025 By Julian Horsey Imagine a world where your AI tools don’t just work for you but work with each other—seamlessly, intelligently, and without the frustration of endless custom integ

AI 73
article thumbnail

Neural Networks Need Kindergarten: Training AI Like Animals Learn

NYU Center for Data Science

Neural networks struggle to learn complex behaviors that come naturally to animals, especially when those behaviors involve sophisticated decision-making over long time scales. Research by NYU Center for Neural Science Postdoctoral Researcher David Hocker , NYU Center for Neural Science Assistant Professor of Neural Science Christine M. Constantinople , and CDS Associate Professor of Neural Science and Data Science Cristina Savin shows that the solution lies in mimicking how animals actually lea

AI 72
article thumbnail

A Guide to Debugging Apache Airflow® DAGs

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate