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Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference Sharon Li Artificial Intelligence

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[[{“value”:” Multimodal fine-tuning represents a powerful approach for customizing vision large language models (LLMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific… Read More »Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference Sharon Li Artificial Intelligence

3 Ways to Speed Up Model Training Without More GPUs Shittu Olumide MachineLearningMastery.com

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In this article, you will learn three proven ways to speed up model training by optimizing precision, memory, and data flow — without adding any… In this article, you will learn three proven ways to speed up model training by optimizing precision, memory, and data flow… Read More »3 Ways to Speed Up Model Training Without More GPUs Shittu Olumide MachineLearningMastery.com

7 Feature Engineering Tricks for Text Data Iván Palomares Carrascosa MachineLearningMastery.com

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An increasing number of AI and machine learning-based systems feed on text data — language models are a notable example today. An increasing number of AI and machine learning-based systems feed on text data — language models are a notable example today. Read More

Bringing AI to the next generation of fusion energy Google DeepMind Blog

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We’re partnering with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality. We’re partnering with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality. Read More

How a Gemma model helped discover a new potential cancer therapy pathway Google DeepMind Blog

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We’re launching a new 27 billion parameter foundation model for single-cell analysis built on the Gemma family of open models. We’re launching a new 27 billion parameter foundation model for single-cell analysis built on the Gemma family of open models. Read More

Training Software Engineering Agents and Verifiers with SWE-Gym Apple Machine Learning Research

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We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model… Read More »Training Software Engineering Agents and Verifiers with SWE-Gym Apple Machine Learning Research

CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals Apple Machine Learning Research

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[[{“value”:”This paper was accepted at the Foundation Models for the Brain and Body Workshop at NeurIPS 2025. Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Leveraging low-power, cost-effective biosignals, e.g. surface electromyography… Read More »CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals Apple Machine Learning Research

Transforming enterprise operations: Four high-impact use cases with Amazon Nova Abhinav Bhargava Artificial Intelligence

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[[{“value”:” Since the launch of Amazon Nova at AWS re:Invent 2024, we have seen adoption trends across industries, with notable gains in operational efficiency, compliance, and customer satisfaction. With its capabilities in secure, multimodal AI and domain customization, Nova is enhancing workflows and enabling cost… Read More »Transforming enterprise operations: Four high-impact use cases with Amazon Nova Abhinav Bhargava Artificial Intelligence

Building smarter AI agents: AgentCore long-term memory deep dive Akarsha Sehwag Artificial Intelligence

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[[{“value”:” Building AI agents that remember user interactions requires more than just storing raw conversations. While Amazon Bedrock AgentCore short-term memory captures immediate context, the real challenge lies in transforming these interactions into persistent, actionable knowledge that spans across sessions. This is the information that… Read More »Building smarter AI agents: AgentCore long-term memory deep dive Akarsha Sehwag Artificial Intelligence

Configure and verify a distributed training cluster with AWS Deep Learning Containers on Amazon EKS Meryem Ozcelik Artificial Intelligence

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[[{“value”:” Training state-of-the-art large language models (LLMs) demands massive, distributed compute infrastructure. Meta’s Llama 3, for instance, ran on 16,000 NVIDIA H100 GPUs for over 30.84 million GPU hours. Amazon Elastic Kubernetes Service (Amazon EKS) is a managed service that simplifies the deployment, management, and… Read More »Configure and verify a distributed training cluster with AWS Deep Learning Containers on Amazon EKS Meryem Ozcelik Artificial Intelligence