KDnuggets
JULY 8, 2025
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 Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python Want to understand how ETL really works?
Data Science Dojo
JULY 7, 2025
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.
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Analytics Vidhya
JULY 8, 2025
The traditional single-modal data approaches often miss important insights that are present in cross-modal relations. Multi-Modal Analysis brings together diverse sources of data, such as text, images, audio, and more similar data to provide a more complete view of an issue. This multi-modal data analysis is called multi-modal data analytics, and it improves prediction accuracy […] The post What is Multi-Modal Data Analysis?
Dataconomy
JULY 9, 2025
French AI startup Mistral is negotiating with investors, including Abu Dhabi’s MGX fund, to secure up to $1 billion in equity funding, according to Bloomberg. Concurrently, Mistral is engaging with French financial institutions, such as Bpifrance SACA, to obtain several hundred million euros in debt financing. These discussions aim to bolster Mistral’s financial position within the global artificial intelligence sector.
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.
Data Science Dojo
JULY 8, 2025
Model Context Protocol (MCP) is rapidly emerging as the foundational layer for intelligent, tool-using AI systems, especially as organizations shift from prompt engineering to context engineering. Developed by Anthropic and now adopted by major players like OpenAI and Microsoft , MCP provides a standardized, secure way for large language models (LLMs) and agentic systems to interface with external APIs, databases, applications, and tools.
ODSC - Open Data Science
JULY 11, 2025
In recent years, data analytics has become a cornerstone of competitive advantage in sports. From Moneyball’s transformative impact on baseball to real-time player tracking in basketball and football, data-driven decision-making is redefining how games are played, coached, and consumed. For data scientists, this presents not only an exciting application area but also a rich source of structured, high-quality datasets perfect for hands-on practice.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Dataconomy
JULY 9, 2025
Machine learning isn’t just a niche tool anymore. It drives decisions that affect billions of dollars and millions of lives. No matter whether you’re approving a loan, forecasting global demand, or suggesting the right seller strategy, the models behind those choices need to be accurate, fair and explainable. That’s where Hatim Kagalwala comes in.
Towards AI
JULY 9, 2025
Last Updated on July 10, 2025 by Editorial Team Author(s): Anubha Bhaik Originally published on Towards AI. Designing a Scalable Multi-Agent AI System for Operational Intelligence Source: Image generated by author using DALL·E In the past year, there’s been a lot of discussion about AI agents — how specialized systems can analyze, plan, and act together to solve problems.
Machine Learning Research at Apple
JULY 9, 2025
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available.
KDnuggets
JULY 10, 2025
Learn the key CLI commands for automated competition submission, downloading and uploading data, running code on free cloud compute, and accessing large AI models.
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!
Dataconomy
JULY 8, 2025
FutureHouse, a research lab co-founded by Sam Rodriques PhD ’19 and Andrew White, is developing an AI platform designed to automate many of the most critical steps in the scientific process. The goal is to address a well-documented problem: scientific productivity is declining. Automating science to reverse declining productivity Over the last few decades, researchers have observed that scientific discovery is becoming slower and more resource-intensive.
Towards AI
JULY 11, 2025
Last Updated on July 12, 2025 by Editorial Team Author(s): Yuval Mehta Originally published on Towards AI. Photo by Kevin Ku on Unsplash Machine learning may be glamorous when you’re tuning models on Kaggle datasets or demoing GPT wrappers. But in production? It’s a grind. You’re not just building a model. You’re building a system, one that takes in unfiltered data from real users, transforms it across distributed nodes, trains a model that doesn’t crash mid-run, and pushes predictions on a dail
AWS Machine Learning Blog
JULY 11, 2025
What if you could replace hours of data analysis with a minute-long conversation? Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting for the correct data sources to manually craft each SQL query.
KDnuggets
JULY 8, 2025
Forget data silos. You can build a modern data lakehouse that gives you transactional consistency, schema evolution, and top-tier performance, all in one place with Apache Iceberg and Apache Spark.
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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.
Dataconomy
JULY 7, 2025
In the realm of artificial intelligence, the emergence of vector databases is changing how we manage and retrieve unstructured data. These specialized systems offer a unique way to handle data through vector embeddings, transforming information into numerical arrays. By allowing for semantic similarity searches, vector databases are enhancing applications across various domains, from personalized content recommendations to advanced natural language processing.
Towards AI
JULY 9, 2025
Last Updated on July 10, 2025 by Editorial Team Author(s): Lihi Gur Arie, PhD Originally published on Towards AI. Introduction Training a high-performing image classifier typically requires large amounts of labeled data. But what if you could achieve top-tier results with minimal data and light training? DINOv2 is a powerful vision foundation model that generates rich image representation vectors, also known as embeddings.
AWS Machine Learning Blog
JULY 11, 2025
In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users, the WWRR organization’s journey toward truly democratized data doesn’t end there.
KDnuggets
JULY 7, 2025
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.
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.
Dataconomy
JULY 7, 2025
Data lakes have emerged as a pivotal solution for handling the vast volumes of raw data generated in today’s data-driven landscape. Unlike traditional storage solutions, data lakes offer a flexibility that allows organizations to store not just structured data, but also unstructured data that varies in type and format. This characteristic empowers businesses in various sectors to harness insights from a wide array of data sources, enabling advanced analytics and data science initiatives.
Towards AI
JULY 10, 2025
Author(s): Harsh Chandekar Originally published on Towards AI. Ever wondered how your friendly neighborhood AI knows that “king” is somewhat similar to “queen” but definitely not to “banana”? The unsung heroes behind this magic are embeddings, and their meticulously organized apartments are vector databases. Think of embeddings as the AI’s internal language — a super-dense, high-dimensional numerical representation of just about anything: text, images, audio, you name it.
PyImageSearch
JULY 7, 2025
Home Table of Contents Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection The YOLO Evolution (Quick Recap) YOLOv8: Introducing the C2f Module and OBB Support YOLOv9: Programmable Gradient Information and GELAN YOLOv10: NMS-Free Training and Dual Assignments YOLOv11: Enhanced Speed with C3K2 Blocks and Official OBB Support Limitations of YOLOv8 to YOLOv11 Why YOLO Avoided Attention (Until Now) The Bottlenecks at a Glance YOLOv12: A New Solution What’s New in YOLOv
KDnuggets
JULY 7, 2025
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.
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
Dataconomy
JULY 9, 2025
A new report from Incogni evaluates the data privacy practices of today’s most widely used AI platforms. As generative AI and large language models (LLMs) become deeply embedded in everyday tools and services, the risk of unauthorized data collection and sharing has surged. Incogni’s researchers analyzed nine leading platforms using 11 criteria to understand which systems offer the most privacy-friendly experience.
JULY 8, 2025
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 Artificial Intelligence Build Interactive Machine Learning Apps with Gradio Create a fun text-to-speech demo in minutes Ehssan Khan Jul 8, 2025 7 min read Share Image by author As a developer working with machine learning models, you likely spend hours writing s
Towards AI
JULY 9, 2025
Author(s): Ojasva Goyal Originally published on Towards AI. The secret sauce has a name — actually, two names: CUDA and cuDNN. Image by Kevin Ache on Unsplash The Superhero Origin Story Picture this: It’s 2006, and NVIDIA realizes their graphics cards have untapped superpowers. They’re not just for making video games look pretty, these GPUs contain thousands of tiny cores that could solve complex problems if only someone would give them the chance.
Analytics Vidhya
JULY 8, 2025
Are you an AI engineer, wondering how to attain resources that can put your skills to a practical test? It might be difficult to look for the right solution for you, based on the vast amount of information out there. Hence, we present this list of all ten GitHub llm repositories every AI engineer ought […] The post 10 GitHub LLM Repositories Every AI Engineer Should Know appeared first on Analytics Vidhya.
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.
ODSC - Open Data Science
JULY 10, 2025
In the rapidly evolving world of AI and data science, platforms are the bridge between promising ideas and real-world impact. Few understand this better than Hugo Shi, co-founder of Saturn Cloud and a technologist whose journey spans quant finance, open-source tooling, and enterprise AI infrastructure. Drawing from his experiences — from working as a quant during the 2008 financial crisis to helping launch Anaconda and now leading Saturn Cloud — Hugo Shi offers valuable insight into how AI tooli
Pickl AI
JULY 9, 2025
Summary: Batch size in deep learning controls how much data a model processes before updating. It impacts training speed, memory, and accuracy. Understanding it helps improve model performance. Learn how steps, epochs, and batch size work together and how to choose the right batch size for your deep learning project. Introduction If you’ve ever trained a deep learning model or even just heard the term thrown around, you’ve likely come across the word batch size.
How to Learn Machine Learning
JULY 6, 2025
In today’s data-driven world, machine learning fuels creativity across industries-from healthcare and finance to e-commerce and entertainment. For many fulfilling roles in data science and analytics, understanding the core machine learning algorithms can be a bit daunting with no examples to rely on. This blog will look at the most popular machine learning algorithms and present real-world use cases to illustrate their application.
Towards AI
JULY 10, 2025
Last Updated on July 12, 2025 by Editorial Team Author(s): Mishtert T Originally published on Towards AI. As Someone who’s spent countless hours debugging code that seemed perfect at first glance, I’ve learned that AI coding tools can be both a blessing and a curse. The question isn’t whether these tools make us faster; they do. The real question is whether they make us better.
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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
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