June, 2025

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Why You Need RAG to Stay Relevant as a Data Scientist

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 You Need RAG to Stay Relevant as a Data Scientist How retrieval-augmented generation (RAG) reduces LLM costs, minimises hallucinations, and keeps you employable in the age of AI.

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20 Behavioral Questions to Ace Your Next Data Science Interview

Analytics Vidhya

Landing a data science role isn’t just about coding and modeling anymore. Interviewers increasingly focus on behavioral questions to assess your problem-solving, communication, and teamworking skills. In this article, we’ll explore what these questions are, why they matter, and how to answer them using proven techniques. I’ll also provide you with 20 sample behavioral questions […] The post 20 Behavioral Questions to Ace Your Next Data Science Interview appeared first on Analyt

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Dealing with Missing Data Strategically: Advanced Imputation Techniques in Pandas and Scikit-learn

Machine Learning Mastery

Missing values appear more often than not in many real-world datasets.

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What’s new with Databricks Unity Catalog at Data + AI Summit 2025

databricks

Four years ago, Databricks saw tremendous complexity in the data landscape: separate catalogs for each platform, siloed governance tools across clouds, and no unified way

AI
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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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How to Learn Math for Data Science: A Roadmap for Beginners

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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 How to Learn Math for Data Science: A Roadmap for Beginners Confused about where to start with data science math?

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Inside the LLM system that reads emails like a cybersecurity analyst

Dataconomy

Phishing emails, those deceptive messages designed to steal sensitive information, remain a significant cybersecurity threat. As attackers devise increasingly sophisticated tactics, traditional detection methods often fall short. Researchers from the University of Auckland, have introduced a novel approach to combat this issue. Their paper, titled “ MultiPhishGuard: An LLM-based Multi-Agent System for Phishing Email Detection ,” authored by Yinuo Xue, Eric Spero, Yun Sing Koh, and Gi

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Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

Hacker News

Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the discovery of novel algorithms, but is limited by first-order improvements and the human design of a suitable search space.

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Normalizing Flows are Capable Generative Models

Machine Learning Research at Apple

Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly performant NF models.

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What are Model Parameters and why do they matter?

Pickl AI

Summary: Model parameters are the internal variables learned from data that define how machine learning models make predictions. Distinct from hyperparameters, they are optimized during training to capture data patterns. Proper initialization and optimization of parameters are crucial for model accuracy, generalization, and efficient learning in AI applications.

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How I Automated My Machine Learning Workflow with Just 10 Lines of Python

Flipboard

The world’s leading publication for data science, AI, and ML professionals. Sign in Sign out Contributor Portal Latest Editor’s Picks Deep Dives Contribute Newsletter Toggle Mobile Navigation LinkedIn X Toggle Search Search Data Science How I Automated My Machine Learning Workflow with Just 10 Lines of Python Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance.

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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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Data exploration

Dataconomy

Data exploration serves as the gateway to understanding the wealth of information hidden within datasets. By employing various techniques and tools, analysts can uncover insights that drive decision-making and improve outcomes across multiple sectors. Through careful examination of data, organizations can identify trends, detect anomalies, and derive strategic advantages.

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Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python

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 Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python Clean and validate messy data with a compact Python pipeline that fits into any workflow.

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Muvera: Making multi-vector retrieval as fast as single-vector search

Hacker News

Jump to Content Research Research Who we are Back to Who we are menu Defining the technology of today and tomorrow. Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more Philosophy People Our researchers drive advancements in computer science through both fundamental and applied research.

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10 Must-Know Python Libraries for MLOps in 2025

Machine Learning Mastery

MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.

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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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Emerging Data Science Trends in 2025 You Need to Know

Pickl AI

Summary: In 2025, data science evolves with trends like augmented analytics, IoT data explosion, advanced machine learning, automation, and explainable AI. These innovations empower businesses to make faster, smarter decisions while ensuring transparency and scalability. Staying updated is vital for professionals and organizations to maintain a competitive edge.

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Accelerate Machine Learning Model Serving With FastAPI and Redis Caching

Analytics Vidhya

Ever waited too long for a model to return predictions? We have all been there. Machine learning models, especially the large, complex ones, can be painfully slow to serve in real time. Users, on the other hand, expect instant feedback. That’s where latency becomes a real problem. Technically speaking, one of the biggest problems is […] The post Accelerate Machine Learning Model Serving With FastAPI and Redis Caching appeared first on Analytics Vidhya.

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Mosaic AI Announcements at Data + AI Summit 2025

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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Automate Data Quality Reports with n8n: From CSV to Professional Analysis

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 Automate Data Quality Reports with n8n: From CSV to Professional Analysis Analyze any CSV dataset from a URL and generate professional quality reports with n8n By Vinod Chugani on June 26, 2025 in Data Science Image by Author | ChatGPT The Data Quali

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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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Unsupervised Elicitation of Language Models

Hacker News

To steer pretrained language models for downstream tasks, today's post-training paradigm relies on humans to specify desired behaviors. However, for models with superhuman capabilities, it is difficult or impossible to get high-quality human supervision. To address this challenge, we introduce a new unsupervised algorithm, Internal Coherence Maximization (ICM), to fine-tune pretrained language models on their own generated labels, emph{without external supervision}.

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I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy

Flipboard

The world’s leading publication for data science, AI, and ML professionals. Sign in Sign out Contributor Portal Latest Editor’s Picks Deep Dives Contribute Newsletter Toggle Mobile Navigation LinkedIn X Toggle Search Search Machine Learning I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy Complete guide to feature selection, threshold optimization, and neural network architecture for ML competitions Claudia Ng Jun 16, 2025 7 min read Share Anime-style illustration of

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Data processing

Dataconomy

Data processing is at the heart of transforming raw numbers into actionable insights that drive decisions across various sectors. In our data-driven world, understanding how vast amounts of information flow through systems enables organizations to harness the right data effectively. What is data processing? Data processing is a systematic approach to converting raw data into meaningful information.

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Evaluating Long-Context Question & Answer Systems

Eugene Yan

eugeneyan Start Here Writing Speaking Prototyping About Evaluating Long-Context Question & Answer Systems [ llm eval survey ] · 28 min read While evaluating Q&A systems is straightforward with short paragraphs, complexity increases as documents grow larger. For example, lengthy research papers, novels and movies, as well as multi-document scenarios.

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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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Mixture of Experts Architecture in Transformer Models

Machine Learning Mastery

This post covers three main areas: • Why Mixture of Experts is Needed in Transformers • How Mixture of Experts Works • Implementation of MoE in Transformer Models The Mixture of Experts (MoE) concept was first introduced in 1991 by

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MLFlow Mastery: A Complete Guide to Experiment Tracking and Model Management

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 MLFlow Mastery: A Complete Guide to Experiment Tracking and Model Management MLFlow is a tool that helps you manage machine learning projects.

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Log-Linear Attention

Hacker News

The attention mechanism in Transformers is an important primitive for accurate and scalable sequence modeling. Its quadratic-compute and linear-memory complexity however remain significant bottlenecks. Linear attention and state-space models enable linear-time, constant-memory sequence modeling and can moreover be trained efficiently through matmul-rich parallelization across sequence length.

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Reinforcement Learning from Human Feedback, Explained Simply

Flipboard

The world’s leading publication for data science, AI, and ML professionals. Sign in Sign out Contributor Portal Latest Editor’s Picks Deep Dives Contribute Newsletter Toggle Mobile Navigation LinkedIn X Toggle Search Search Large Language Models Reinforcement Learning from Human Feedback, Explained Simply The one technique that made ChatGPT so smart Vyacheslav Efimov Jun 23, 2025 7 min read Share Introduction The appearance of ChatGPT in 2022 completely changed how the world started perceiving a

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The 2nd Generation of Innovation Management: A Survival Guide

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?

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Image recognition

Dataconomy

Image recognition is transforming how we interact with technology, enabling machines to interpret and identify what they see, similar to human vision. This remarkable capability has applications ranging from security and healthcare to social media and augmented reality. Understanding how this technology works can provide valuable insights into its potential and implications.

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Think Your Code Model Is Smart? Interactive Benchmarks Might Say Otherwise

NYU Center for Data Science

Letting models receive human-style feedback changed which ones ranked best by up to four spots. That is what Courant PhD student Jane Pan , CDS PhD student Jacob Pfau , CDS Assistant Professor He He , and colleagues showed in “ When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback ,” which described a way to replace static coding benchmarks like HumanEval, MBPP, APPS and CodeXGLUE with a multi-step, human-in-the-loop evaluation.

AI
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AI’s Bright Future: Insights from ODSC East 2025 Podcast Minisodes

ODSC - Open Data Science

ODSC East 2025 once again delivered a powerhouse of AI insights, featuring a unique podcast episode recorded live with short interviews from some of the brightest minds in AI today. Across these minisodes, speakers explored cutting-edge topics ranging from AI agents, small language models, and AI risk management, to synthetic data, causal AI, and even social media algorithms.

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Top 5 Frameworks for Distributed Machine Learning

KDnuggets

Use these frameworks to optimize memory and compute resources, scale your machine learning workflow, speed up your processes, and reduce the overall cost.

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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