Thu.Jan 30, 2025

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How to Run Parallel Time Series Analysis with Dask

KDnuggets

In this article, we show you how to run parallel time series analysis with Dask, through a practical Python-based tutorial.

Python 121
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Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data

Towards Data Science

Building more efficient AI TLDR : Data-centric AI can create more efficient and accurate models. I experimented with data pruning on MNIST to classify handwritten digits. Best runs for furthest-from-centroid selection compared to full dataset. Image byauthor. What if I told you that using just 50% of your training data could achieve better results than using the fulldataset?

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How to Summarize Scientific Papers Using the BART Model with Hugging Face Transformers

KDnuggets

Learn how to perform paper summarization with BART.

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AWS Lambda + DuckDB + Polars + Daft + Rust

Confessions of a Data Guy

When it comes to building modern Lake House architecture, we often get stuck in the past, doing the same old things time after time. We are human; we are lemmings; it’s just the trap we fall into. Usually, that pit we fall into is called Spark. Now, don’t get me wrong; I love Spark. We […] The post AWS Lambda + DuckDB + Polars + Daft + Rust appeared first on Confessions of a Data Guy.

AWS 100
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Apache Airflow® 101 Essential Tips for Beginners

Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.

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MySQL at Uber (2025)

Uber Engineering

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MySQL 80

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Smart Utilities in Action: Generative AI’s Role in Real-Time Fault Detection

RandomTrees

The energy and utility industry is being transformed by AI technology, and it is powered by the digital revolution. One of its newest forms, Generative AI, is bolstering utility operations reliability, efficiency, and resilience. Its place in modern utilities is most evident in real-time fault detection. The utilization of Generative AI for utilities is discussed in this article, alongside smart utilities with AI , real-time monitoring AI, and AI predictive maintenance.

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Picnic’s Page Platform from a Mobile perspective: enabling fast updates through server-driven UI

Picnic Engineering

After introducing our Page Architecture initiative in this previous post , well now dive deeper into how we transformed the mobile appthe primary platform where millions of customers do their grocery shopping with Picnic. As an online-only supermarket, the app isnt just another sales channelits the core of all customer experience. This transformation isnt just about technical improvementsits about fundamentally changing how we deliver rich, dynamic user interfaces to customers.

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Real-Time AI for Crisis Management: Responding Faster with Smarter Systems

Striim

During a crisiswhether its a pandemic, a natural disaster, or a major supply chain breakdownswift, informed decision-making can mean the difference between regaining control and facing further escalation. Todays organizations have access to more data than ever before, and consequently are faced with the challenge of determining how to transform this tremendous stream of real-time information into actionable insights.

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Stop Creating Bad DAGs — Optimize Your Airflow Environment By Improving Your Python Code

Towards Data Science

Stop Creating Bad DAGsOptimize Your Airflow Environment By Improving Your PythonCode Valuable tips to reduce your DAGs parse time and save resources. Photo by Dan Roizer on Unsplash Apache Airflow is one of the most popular orchestration tools in the data field, powering workflows for companies worldwide. However, anyone who has already worked with Airflow in a production environment, especially in a complex one, knows that it can occasionally present some problems and weirdbugs.

Python 48
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Apache Airflow® Best Practices: DAG Writing

Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!

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How LLMs and AI Are Shaping Medical Diagnosis

WeCloudData

TThe integration of Artificial Intelligence (AI) and Large Language Models (LLMs), into medical diagnosis healthcare is revolutionizing patient care. But how effective are these tools when it comes to diagnosing complex medical conditions? A recent study conducted by UVA Health, in collaboration with Stanford and Harvard, dives into the diagnostic potential of AI and offers […] The post How LLMs and AI Are Shaping Medical Diagnosis appeared first on WeCloudData.

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Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data

Towards Data Science

How much data does AI reallyneed? TLDR : Data-centric AI can create more efficient and accurate models. I experimented with data pruning on MNIST to classify handwritten digits. Best runs for furthest-from-centroid selection compared to full dataset. Image byauthor. What if I told you that using just 50% of your training data could achieve better results than using the fulldataset?

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How Singapore Embraces Data Streaming Across Finance, Air Travel & More

Confluent

Read this Data in Motion Tour recap to get highlights and key insights from Singaporean business leaders leveraging data streaming in their organizations.

Finance 40
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Stop Creating Bad DAGs — Optimize Your Airflow Environment By Improving Your Python Code

Towards Data Science

Stop Creating Bad DAGsOptimize Your Airflow Environment By Improving Your PythonCode Valuable tips to reduce your DAGs parse time and save resources. Photo by Dan Roizer on Unsplash Apache Airflow is one of the most popular orchestration tools in the data field, powering workflows for companies worldwide. However, anyone who has already worked with Airflow in a production environment, especially in a complex one, knows that it can occasionally present some problems and weirdbugs.

Python 40
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Apache Airflow® Crash Course: From 0 to Running your Pipeline in the Cloud

With over 30 million monthly downloads, Apache Airflow is the tool of choice for programmatically authoring, scheduling, and monitoring data pipelines. Airflow enables you to define workflows as Python code, allowing for dynamic and scalable pipelines suitable to any use case from ETL/ELT to running ML/AI operations in production. This introductory tutorial provides a crash course for writing and deploying your first Airflow pipeline.

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Modern Data Governance: Trends for 2025

Precisely

Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful data governance. Recognize that artificial intelligence is a data governance accelerator and a process that must be governed to monitor ethical considerations and risk. Integrate data governance and data quality practices to create a seamless user experience and build trust in your data.

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Top Gen AI Use Cases: How to Turn Unstructured Data into Insights

Snowflake

Across all industries, generative AI is driving innovation and transforming how we work. Use cases range from getting immediate insights from unstructured data such as images, documents and videos, to automating routine tasks so you can focus on higher-value work. Gen AI makes this all easy and accessible because anyone in an enterprise can simply interact with data by using natural language.