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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.
Explore these top machine learning repositories to build your skills, portfolio, and creativity through hands-on projects, real-world challenges, and AI resources.
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.
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
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.
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.
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.
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.
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
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.
Researchers at KAIST AI and Mila have introduced a new Transformer architecture that makes large language models (LLMs) more memory- and compute-efficient.
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!
The European Union implemented its AI Act last year, releasing guidelines to ensure compliance and balance AI innovation with safety, culminating in the July 18 launch of the AI Act Explorer, a comprehensive guide for companies navigating these regulations. The AI Act, established to introduce safeguards for advanced artificial intelligence models while simultaneously cultivating a competitive and innovative ecosystem for AI enterprises, delineates distinct risk classifications for various model
A hands-on guide to building and validating LLM evaluatorsThe post How to Create an LLM Judge That Aligns with Human Labels appeared first on Towards …
Autonomous AI agents are quickly emerging as “digital coworkers” in multiple workstreams. This includes inside and outside of traditional analytics teams. These new coworkers are handling complex, data-driven tasks with minimal human oversight. Powered by frameworks like AutoGPT, LangChain, and ReAct, these agents reason, learn, and act autonomously, transforming workflows across healthcare, finance, software development, and beyond.
Nvidia announced CUDA platform support for the RISC-V instruction set architecture (ISA) at the 2025 RISC-V Summit in China, enabling RISC-V to serve as a main processor for CUDA-based systems. This development permits RISC-V to function as the primary processor for systems utilizing CUDA, a role previously exclusive to x86 and Arm core architectures.
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.
Everybody has witnessed chatbots in action; some are impressive, while others are annoying. However, what if you could create one that is genuinely intelligent, well-organized, and simple to integrate with your own application? We’ll use two potent tools in this article to build a chatbot from scratch: We’ll begin with a brief setup, which involves […] The post Building a Conversational AI App with Django and LangGraph appeared first on Analytics Vidhya.
The explosion of generative AI tools has changed the game. But here’s the catch: it has become a fragmented maze. Creatives, developers, and AI practitioners are increasingly burdened by a proliferation of tools, platforms, and models, each designed for specific tasks, yet often lacking interoperability or cohesive integration. According to Grand View Research, the global artificial intelligence market was valued at USD 279.22 billion in 2024 and is expected to grow to USD 1,811.75 billion
Home Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training YOLOv12 for Pothole Detection Configure Your API and Install Dependencies Install Dependencies Download the Dataset from Roboflow Fine-Tune the YOLOv12 Model Training Results and Performance Analysis Loss a
Exploratory v13 — Making Analytics Truly Accessible for Everyone Guided Analytics, AI Summary, and more! I’m very excited to announce Exploratory v13! 🎉🎉🎉 At Exploratory, we’ve always believed that powerful analytics shouldn’t require an expert level knowledge in statistics. With open-source technologies and easy-to-use UI tools like Exploratory, running analytics is no longer the hard part.
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.
Generative AI, especially large language models (LLMs), present exciting and unprecedented opportunities and complex challenges for academic research …
A single misconfigured database exposed over 100 million customer records from a fast-scaling startup – and it took months before anyone noticed. While innovation surges ahead, security often lags dangerously behind. The question is urgent: How can fast-growing tech startups protect their data before their growth becomes their greatest risk? When funding rounds accelerate and product sprints dominate the roadmap, security rarely makes it past the backlog.
Artificial intelligence software is designing novel experimental protocols that improve upon the work of human physicists, although the humans are still “doing a lot of baby-sitting.
South Korean AI chip startup FuriosaAI announced a partnership on Tuesday to supply its AI chip, RNGD, to enterprises using LG AI Research‘s recently unveiled EXAONE platform.
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
Fraud remains a huge challenge for governments and inspectors at all levels, as fraudsters today are more successful than ever. There is tremendous potential for technologies such as analytics and AI to support investigations. Why are fraudsters so successful these days? Partly because they seek easy targets, work hard to [.] The post Why advanced analytics has a vital role in the fight against social fraud appeared first on SAS Blogs.
Beneath Yellowstone’s stunning surface lies a hyperactive seismic world, now better understood thanks to machine learning. Researchers have uncovered over 86,000 earthquakes—10 times more than previously known—revealing chaotic swarms moving along rough, young fault lines. With these new insights, we’re getting closer to decoding Earth’s volcanic heartbeat and improving how we predict and manage volcanic and geothermal hazards.
Yahoo Japan is mandating the use of generative AI for all 11,000 employees to double productivity by 2028, integrating AI into daily tasks to shift focus towards higher-level thinking and communication, according to Techradar. The company’s directive requires all staff to integrate generative AI technologies and tools into their daily assignments and operations.
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.
Small and medium-sized businesses are gaining a significant competitive edge by adopting artificial intelligence, reclaiming the equivalent of a full workday each week. According to a new survey from marketing technology firm ActiveCampaign, AI-powered systems are not only saving time but also reducing operational costs by thousands of dollars monthly.
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Nosa Omoigui, CEO of Weave.AI, neuro-symbolic GenAI and intelligent agents that transform alpha decision making and risk analysis. As artificial intelligence (AI) accelerates across the financial sector, a critical question remains: Can executives trust the answers AI provides?
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: 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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