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8 Ways to Scale your Data Science Workloads

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 8 Ways to Scale your Data Science Workloads From in-spreadsheet machine learning to terabyte sized DataFrames, learn how to stop fighting your tools and focus on solving problems.

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FastVLM: Efficient Vision Encoding for Vision Language Models

Machine Learning Research at Apple

Vision Language Models (VLMs) enable visual understanding alongside textual inputs. They are typically built by passing visual tokens from a pretrained vision encoder to a pretrained Large Language Model (LLM) through a projection layer. By leveraging the rich visual representations of the vision encoder and the world knowledge and reasoning capabilities of the LLM, VLMs can be useful for a wide range of applications, including accessibility assistants, UI navigation, robotics, and gaming.

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Agentic RAG: A Powerful Leap Forward in Context-Aware AI

Data Science Dojo

Retrieval-augmented generation (RAG) has already reshaped how large language models (LLMs) interact with knowledge. But now, we’re witnessing a new evolution: the rise of RAG agents —autonomous systems that don’t just retrieve information, but plan, reason, and act. In this guide, we’ll walk through what a rag agent actually is, how it differs from standard RAG setups, and why this new paradigm is redefining intelligent problem-solving.

AI 261
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From Architecture to Execution: Inside Week 2 of the Agentic AI Summit

ODSC - Open Data Science

The second week of the Agentic AI Summit built upon week 1 by diving deeper into the engineering realities of agentic AI — from protocol-level orchestration to agent deployment inside enterprise environments and even developer IDEs. Leaders from Monte Carlo, TrueFoundry, LlamaIndex, TripAdvisor, and more shared how they’re moving from prototypes to production, surfacing the tools, patterns, and challenges they’ve encountered along the way.

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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.

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Building a Plain Seq2Seq Model for Language Translation

Machine Learning Mastery

This post is divided into five parts; they are: • Preparing the Dataset for Training • Implementing the Seq2Seq Model with LSTM • Training the Seq2Seq Model • Using the Seq2Seq Model • Improving the Seq2Seq Model In

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Why Python Pros Avoid Loops: A Gentle Guide to Vectorized Thinking

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 Why Python Pros Avoid Loops: A Gentle Guide to Vectorized Thinking Loops are easy to write, but vectorized operations are the secret to writing efficient and elegant Python code.

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Rust running on every GPU

Hacker News

Skip to main content Rust GPU Docs Blog Ecosystem Changelog GitHub Recent posts 2025 Rust running on every GPU Porting GPU shaders to Rust 30x faster with AI Rust CUDA May 2025 project update Shadertoys ported to Rust GPU Rust CUDA project update Rust running on every GPU July 25, 2025 · 19 min read Christian Legnitto Rust GPU and Rust CUDA maintainer Ive built a demo of a single shared Rust codebase that runs on every major GPU platform: CUDA for NVIDIA GPUs SPIR-V for Vulkan-compatible GPUs fr

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Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models

Machine Learning Research at Apple

Aligned representations across languages is a desired property in multilingual large language models (mLLMs), as alignment can improve performance in cross-lingual tasks. Typically alignment requires fine-tuning a model, which is computationally expensive, and sizable language data, which often may not be available. A data-efficient alternative to fine-tuning is model interventions -- a method for manipulating model activations to steer generation into the desired direction.

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Synthetic Dataset Generation with Faker

Machine Learning Mastery

In this article, you will learn: • how to use the Faker library in Python to generate various types of synthetic data.

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10 GitHub Repositories for Machine Learning Projects

KDnuggets

Explore these top machine learning repositories to build your skills, portfolio, and creativity through hands-on projects, real-world challenges, and AI resources.

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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!

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From Chaos to Control: A Cost Maturity Journey with Databricks

databricks

Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions

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How Do LLMs Work? Discover the Hidden Mechanics Behind ChatGPT

Data Science Dojo

How do LLMs work? It’s a question that sits at the heart of modern AI innovation. From writing assistants and chatbots to code generators and search engines, large language models (LLMs) are transforming the way machines interact with human language. Every time you type a prompt into ChatGPT or any other LLM-based tool, you’re initiating a complex pipeline of mathematical and neural processes that unfold within milliseconds.

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On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization

Machine Learning Research at Apple

The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization.

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The Role of LLMs in Managing Unstructured Data

ODSC - Open Data Science

Businesses constantly generate unstructured data like emails, reports, customer chats, and social media posts. Because it doesn’t follow a fixed format, this data type is often challenging to organize, analyze, or use effectively with traditional tools. Large language models , a form of AI trained on vast collections of text, are changing that. With their ability to understand and generate human language, LLMs give organizations new ways to unlock insights, automate processes, and helping with m

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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.

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10 Python One-Liners for JSON Parsing and Processing

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 10 Python One-Liners for JSON Parsing and Processing Crack complex JSON with these Python one-liners that do the heavy lifting.

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Setting Up a Machine Learning Pipeline on Google Cloud Platform

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 Setting Up a Machine Learning Pipeline on Google Cloud Platform Learn the steps for setting up the machine learning pipeline in the top cloud provider.

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Distillation makes AI models smaller and cheaper

Hacker News

Fundamental technique lets researchers use a big, expensive “teacher” model to train a “student” model for less.

AI 181
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Introducing Recursive Common Table Expressions to Databricks

databricks

Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions

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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.

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Random numbers

Dataconomy

Random numbers are a fascinating aspect of mathematics and computer science, often playing a crucial role in applications like cryptography, statistical analysis, and computer simulations. This article explores the intricacies of random numbers, their characteristics, and methods of generation, as well as their diverse applications and the challenges associated with achieving true randomness.

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How to Build Scalable ML Pipelines on Snowflake

phData

As datasets grow and the need for machine learning (ML) solutions expands, scaling ML pipelines presents increasing complexities. Feature engineering can become time-consuming, model training can take longer, and the demands of managing computational infrastructure can all be blockers for business requirements. Snowflake AI Data Cloud addresses these challenges by providing ML Objects on its unified platform, allowing ML workflows to scale efficiently.

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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data

Flipboard

In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using …

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How to Migrate from OpenAI to Cerebrium for Cost-Predictable AI Inference

Hacker News

Skip to content Ritza Articles How To Migrate From OpenAI to Cerebrium for Cost-Predictable AI Inference Ritza Articles Ritza articles AI-generated books are flooding Amazon (and theyre as reliable as you would guess) Analysing 10 years of who is hiring HN data Best Python Books For Beginners **Clubhouse summaries**: @shl and @jasonfried discuss deadlines Comparing Business Bank Accounts in South Africa How to create a simple survey app with React using Next.js and Sanity Creating Custom Graphs

AI 138
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Automation, Evolved: Your New Playbook for Smarter Knowledge Work

Speaker: Frank Taliano

Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.

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What Are Large Language Models (LLMs)?

Pickl AI

Summary: Large language models (LLMs) are reshaping data science through automation, language generation, and real-time analytics. From customer service to fraud detection, LLMs drive efficiency, insight, and innovation. This guide explores how LLMs work, their applications, and use cases across industries, helping individuals and businesses unlock their full potential.

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10 Free Online Courses to Master Python in 2025

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 10 Free Online Courses to Master Python in 2025 How can you master Python for free? Here are ten online courses we recommend.

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What’s New: Lakeflow Jobs Provides More Efficient Data Orchestration

databricks

Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions

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Mixture-of-recursions delivers 2x faster inference—Here’s how to implement it

Flipboard

Researchers at KAIST AI and Mila have introduced a new Transformer architecture that makes large language models (LLMs) more memory- and compute-efficient.

AI 172
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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

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Subliminal learning: Models transmit behaviors via hidden signals in data

Hacker News

Alignment Science Blog Subliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data Alex Cloud* 1 , Minh Le* 1 , July 22, 2025 James Chua 2 , Jan Betley 2 , Anna Sztyber-Betley 3 , Jacob Hilton 4 , Samuel Marks 5 , Owain Evans 2,6 *Equal contribution; author order chosen randomly 1 Anthropic Fellows Program; 2 Truthful AI; 3 Warsaw University of Technology; 4 Alignment Research Center; 5 Anthropic; 6 UC Berkeley tl;dr We study subliminal learning , a surprising phen

AI 139
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MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains

Machine Learning Research at Apple

Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. This lack of granularity makes it difficult to deeply discern where failures stem from.

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Qwen’s new AI is a fully autonomous coding agent

Dataconomy

Qwen has released Qwen3-Coder-480B-A3.5B-Instruct , an open agentic code model, to enhance software development efficiency and accuracy by integrating advanced algorithms and a vast knowledge base. The Qwen3-Coder-480B-A3.5B-Instruct model represents an advancement in AI-driven coding assistance. It is specifically engineered to improve the effectiveness and precision of software development workflows.

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A Complete Guide to Matplotlib: From Basics to Advanced Plots

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 A Complete Guide to Matplotlib: From Basics to Advanced Plots Master Matplotlib basics to advanced plots with this guide.

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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.