Data integration at a deep iPaaS level can help feed AI services with the right data, the correct langauge models and the most relevant information sources. MORE
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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 Serve Machine Learning Models via REST APIs in Under 10 Minutes Stop leaving your models on your laptop.
Context engineering is quickly becoming the new foundation of modern AI system design, marking a shift away from the narrow focus on prompt engineering. While prompt engineering captured early attention by helping users coax better outputs from large language models (LLMs), it is no longer sufficient for building robust, scalable, and intelligent applications.
By Kamal Hathi, SVP and GM, Splunk Products & Technology Today’s fast-evolving digital landscape, especially with the explosive growth of AI, has rapidly added to the complexity of data management. This growing dependence on AI has not only added to complexity, but also transformed strategic data management from a competitive advantage into a business imperative.
Today, we’re introducing the Databricks AI Governance Framework (DAGF v1.0), a structured and practical approach to governing AI adoption across the enterprise.
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.
Recommendation systems are everywhere. From Netflix and Spotify to Amazon. But what if you wanted to build a visual recommendation engine? One that looks at the image, not just the title or tags? In this article, you’ll build a men’s fashion recommendation system. It will use image embeddings and the Qdrant vector database. You’ll go […] The post Build a Men’s Fashion Recommendation System Using FastEmbed and Qdrant appeared first on Analytics Vidhya.
A philosophical divergence between Meta CEO Mark Zuckerberg and Chief AI Scientist Yann LeCun regarding artificial intelligence strategy and timelines became evident last week with the announcement of Meta Superintelligence Labs , generating uncertainty about the company’s future AI direction. This division within Meta’s AI teams centers on fundamental approaches to AI development.
A philosophical divergence between Meta CEO Mark Zuckerberg and Chief AI Scientist Yann LeCun regarding artificial intelligence strategy and timelines became evident last week with the announcement of Meta Superintelligence Labs , generating uncertainty about the company’s future AI direction. This division within Meta’s AI teams centers on fundamental approaches to AI development.
Today, we open sourced our Zero-Knowledge Proof (ZKP) libraries, fulfilling a promise and building on our partnership with Sparkasse to support EU age assurance.
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.
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.
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 Repositories for Mastering Agents and MCPs Learn how to build your own agentic AI application with free tutorials, guides, courses, projects, example code, research papers, and more.
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.
AI integration is expanding into the Linux command line, exemplified by tools like Ollama, making its presence in this environment increasingly common. The Gemini CLI tool enables users to access Google’s Gemini AI directly within their Linux terminal. This locally installed application supports various functions, including content generation, problem-solving, detailed research, and task management.
New York, July 2, 2025 – AI-based workflow automation company fileAI today announced the launch of its public platform designed to help enterprises and SMBs understand and collect business data trapped in unstructured formats, siloed systems, disconnected databases and externally.
Introduction Large Language Models (LLMs) have swiftly become essential components of modern workflows, automating tasks traditionally performed by humans.
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.
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 5 Fun Python Projects for Absolute Beginners Bored of theory? These hands-on Python projects make learning interactive, practical, and actually enjoyable.
Google has open-sourced its Zero-Knowledge Proof (ZKP) libraries, delivering on a commitment and leveraging a partnership with Sparkasse to support age assurance within the European Union. This initiative aims to facilitate the development of privacy-enhancing applications and digital identity solutions by developers in both private and public sectors, addressing a pressing demand.
Have you ever been stuck in a situation where you have a huge dataset and you wanted insights from it? Sounds scary, right? Getting useful insights, especially from a huge dataset, is a tall order. Imagine transforming your dataset into an interactive web application without any frontend expertise for data visualization. Gradio, when used alongside […] The post 9 Steps for Crafting an Interactive Dashboard using Python and Gradio appeared first on Analytics Vidhya.
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
To better understand human cognition, scientists trained a large language model on 10 million psychology experiment questions. It now answers questions much like we do. Companies like OpenAI and Meta are in a race to make something they like to call artificial general intelligence.
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 7 DuckDB SQL Queries That Save You Hours of Pandas Work See how DuckDB outperforms Pandas in real world tasks like filtering, cohort analysis and revenue modelling all within your notebook.
TYSONS CORNER, Va., July 3, 2025 — Today, Available Infrastructure unveiled SanQtum, which combines national security-grade cyber protection and trusted enterprise artificial intelligence (AI) capability, according to the company. In the modern era, AI-powered, machine-speed decision-making is crucial.
The recent rapid adoption of large language models (LLMs) highlights the critical need for benchmarking their fairness. Conventional fairness metrics, which focus on discrete accuracy-based evaluations (i.e., prediction correctness), fail to capture the implicit impact of model uncertainty (e.g., higher model confidence about one group over another despite similar accuracy).
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.
Last week, I took the stage at one of the nation’s premier AI conferences – SSON Intelligent Automation Week 2025 to deliver some uncomfortable truths about enterprise RAG. What I shared about the 42% increase in failure rate caught even seasoned practitioners off guard. Here’s what I told them , and why it matters for every […] The post The 5 Silent Killers of Production RAG appeared first on Analytics Vidhya.
LLMs — the data models powering your favorite AI chatbots — don't just have social and racial biases, a new report finds, but inherent biases against democratic institutions. A recent study , published by researchers at the MIT Sloan School of Management , analyzed how six popular LLMs (including ChatGPT, Gemini, and DeepSeek) portray the state of press freedom — and, indirectly, trust in the media — in responses to user prompts.
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
Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes settings, it becomes vital to know when the output of an LLM may be unreliable. Depending on whether an answer is trustworthy, a system can then choose to route the question to another expert, or otherwise fall back on a safe default behavior.
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
Skip to Content MIT Technology Review Featured Topics Newsletters Events Audio Sign in Subscribe MIT Technology Review Featured Topics Newsletters Events Audio Sign in Subscribe Opinion Don’t let hype about AI agents get ahead of reality There is enormous potential for this technology, but only if we deploy it responsibly. By Yoav Shoham archive page July 3, 2025 Sarah Rogers/MITTR | Getty Google’s recent unveiling of what it calls a “new class of agentic experiences” feels like a turning point.
Speaker: Chris Townsend, VP of Product Marketing, Wellspring
Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?
Data integration at a deep iPaaS level can help feed AI services with the right data, the correct langauge models and the most relevant information sources.
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