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Accelerating UMAP: Processing 10 Million Records in Under a Minute With No Code Changes

ODSC - Open Data Science

It dramatically improves algorithm performance for data-intensive tasks involving tens to hundreds of millions of records. cuML can make complex, iterative workflows possible, such as for single cell genomics analysis, topic modeling, anomaly detection and more.

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Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

AWS Machine Learning Blog

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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How predictive analytics are shaping search strategies

Dataconomy

Regression models estimate relationships between variables, making them useful for forecasting future performance. Time series models analyse data points collected over time. Clustering models group similar data together, assisting with understanding customer behaviour prediction and market segments.

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Carnegie Mellon University at ICML 2025

ML @ CMU

Paprika trains models on synthetic environments requiring different exploration behaviors, encouraging them to learn flexible strategies rather than memorizing solutions. To improve efficiency, it uses a curriculum learning-based approach that prioritizes tasks with high learning value, making the most of limited interaction data.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Apache HBase was employed to offer real-time key-based access to data. This created a challenge for data scientists to become productive. HBase is employed to offer real-time key-based access to data.

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Graph visualization UX: Designing intuitive data experiences

Cambridge Intelligence

You can prevent this by redesigning the data model, limiting expansion, grouping less important nodes, or even removing the central node entirely and indicating connections to it through glyphs or other styling. Unclear labels – Apply smart truncation and tooltips.

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Benchmarking Volga’s On-Demand Compute Layer for Feature Serving: Latency, RPS, and Scalability on EKS

Towards AI

Tests setup We ran load tests on an Amazon EKS cluster using t2.medium medium instances (2 vCPUs, 4 GB RAM), hosting both the Locust deployment and the Ray cluster running Volga. Each Ray pod was mapped to a single EKS node to ensure resource isolation.