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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.
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.
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.
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.
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.
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
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.
In many enterprise scenarios, SharePoint-hosted Excel files serve as the bridge between raw data and business operations. But keeping them up to date, especially when your data lives in Azure Synapse , can be surprisingly difficult due to limitations in native connectors. In this guide, you’ll learn a step-by-step method to build a no-code/low-code Azure Synapse to SharePoint Excel automation using Power BI and Power Automate.
In many enterprise scenarios, SharePoint-hosted Excel files serve as the bridge between raw data and business operations. But keeping them up to date, especially when your data lives in Azure Synapse , can be surprisingly difficult due to limitations in native connectors. In this guide, you’ll learn a step-by-step method to build a no-code/low-code Azure Synapse to SharePoint Excel automation using Power BI and Power Automate.
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
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.
Explore these top machine learning repositories to build your skills, portfolio, and creativity through hands-on projects, real-world challenges, and AI resources.
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!
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
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.
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.
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
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.
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.
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.
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
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.
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.
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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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.
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.
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.
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
Researchers at KAIST AI and Mila have introduced a new Transformer architecture that makes large language models (LLMs) more memory- and compute-efficient.
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
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
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.
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.
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.
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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