Category: News

The Machine Learning “Advent Calendar” Day 15: SVM in ExcelTowards Data Science Instead of starting with margins and geometry, this article builds the Support Vector Machine step by step from familiar models. By changing the loss function and reusing regularization, SVM appears naturally as a linear classifier trained by optimization. This perspective unifies logistic regression, SVM, and other linear models into a single, coherent framework.
The post The Machine Learning “Advent Calendar” Day 15: SVM in Excel appeared first on Towards Data Science.

 Instead of starting with margins and geometry, this article builds the Support Vector Machine step by step from familiar models. By changing the loss function and reusing regularization, SVM appears naturally as a linear classifier trained by optimization. This perspective unifies logistic regression, SVM, and other linear models into a single, coherent framework.
The post The Machine Learning “Advent Calendar” Day 15: SVM in Excel appeared first on Towards Data Science. Read More  

Adaptive infrastructure for foundation model training with elastic training on SageMaker HyperPodArtificial Intelligence Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource adaptation, while maintain training quality and minimizing manual intervention.

 Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource adaptation, while maintain training quality and minimizing manual intervention. Read More  

Customize agent workflows with advanced orchestration techniques using Strands AgentsArtificial Intelligence In this post, we explore two powerful orchestration patterns implemented with Strands Agents. Using a common set of travel planning tools, we demonstrate how different orchestration strategies can solve the same problem through distinct reasoning approaches,

 In this post, we explore two powerful orchestration patterns implemented with Strands Agents. Using a common set of travel planning tools, we demonstrate how different orchestration strategies can solve the same problem through distinct reasoning approaches, Read More  

Operationalize generative AI workloads and scale to hundreds of use cases with Amazon Bedrock – Part 1: GenAIOpsArtificial Intelligence In this first part of our two-part series, you’ll learn how to evolve your existing DevOps architecture for generative AI workloads and implement GenAIOps practices. We’ll showcase practical implementation strategies for different generative AI adoption levels, focusing on consuming foundation models.

 In this first part of our two-part series, you’ll learn how to evolve your existing DevOps architecture for generative AI workloads and implement GenAIOps practices. We’ll showcase practical implementation strategies for different generative AI adoption levels, focusing on consuming foundation models. Read More  

Applying data loading best practices for ML training with Amazon S3 clientsArtificial Intelligence In this post, we present practical techniques and recommendations for optimizing throughput in ML training workloads that read data directly from Amazon S3 general purpose buckets.

 In this post, we present practical techniques and recommendations for optimizing throughput in ML training workloads that read data directly from Amazon S3 general purpose buckets. Read More  

The Data Detox: Training Yourself for the Messy, Noisy, Real WorldKDnuggets In this article, we’ll use a real-life data project to explore four practical steps for preparing to deal with messy, real-life datasets.

 In this article, we’ll use a real-life data project to explore four practical steps for preparing to deal with messy, real-life datasets. Read More  

6 Technical Skills That Make You a Senior Data ScientistTowards Data Science Beyond writing code, these are the design-level decisions, trade-offs, and habits that quietly separate senior data scientists from everyone else.
The post 6 Technical Skills That Make You a Senior Data Scientist appeared first on Towards Data Science.

 Beyond writing code, these are the design-level decisions, trade-offs, and habits that quietly separate senior data scientists from everyone else.
The post 6 Technical Skills That Make You a Senior Data Scientist appeared first on Towards Data Science. Read More  

How Transformers Think: The Information Flow That Makes Language Models WorkKDnuggets Let’s uncover how transformer models sitting behind LLMs analyze input information like user prompts and how they generate coherent, meaningful, and relevant output text “word by word”.

 Let’s uncover how transformer models sitting behind LLMs analyze input information like user prompts and how they generate coherent, meaningful, and relevant output text “word by word”. Read More