​[[{“value”:” Building large language model (LLM)-powered applications for real-world production scenarios is challenging. Developers often face issues such as inconsistent responses from models, difficulties in ensuring robustness, and a lack of strong type safety. When building applications that leverage LLMs, the goal is to provide… Read More »Meet PydanticAI: A New Python-based Agent Framework to Build Production-Grade LLM-Powered Applications Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The static knowledge base and hallucination-creating inaccuracy or fabrication of information are two common issues with large language models (LLMs). The parametric knowledge within LLMs is inherently static, making it challenging to provide up-to-date information in real-time scenarios. Retrieval-augmented generation (RAG) addresses the problem… Read More »DMQR-RAG: A Diverse Multi-Query Rewriting Framework Designed to Improve the Performance of Both Document Retrieval and Final Responses in RAG Nazmi Syed Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity and diversity of physical systems. Many existing datasets are limited, often covering only small classes of physical behaviors. This lack… Read More »Polymathic AI Releases ‘The Well’: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical Systems Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” This post is co-written with Abhishek Sawarkar, Eliuth Triana, Jiahong Liu and Kshitiz Gupta from NVIDIA.  At re:Invent 2024, we are excited to announce new capabilities to speed up your AI inference workloads with NVIDIA accelerated computing and software offerings on Amazon SageMaker. These… Read More »Speed up your AI inference workloads with new NVIDIA-powered capabilities in Amazon SageMaker Abhishek Sawarkar AWS Machine Learning Blog

​[[{“value”:” Today at AWS re:Invent 2024, we are excited to announce a new feature for Amazon SageMaker inference endpoints: the ability to scale SageMaker inference endpoints to zero instances. This long-awaited capability is a game changer for our customers using the power of AI and… Read More »Unlock cost savings with the new scale down to zero feature in SageMaker Inference Marc Karp AWS Machine Learning Blog

​[[{“value”:” Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI  models for inference. This innovation allows you to scale your models faster, observing up to 56%… Read More »Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference Wenzhao Sun AWS Machine Learning Blog

​[[{“value”:” The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. These models have grown exponentially in size and complexity, with some now containing hundreds of billions of parameters and requiring hundreds of gigabytes of memory.… Read More »Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 Lokeshwaran Ravi AWS Machine Learning Blog

​[[{“value”:” In Part 1 of this series, we introduced Amazon SageMaker Fast Model Loader, a new capability in Amazon SageMaker that significantly reduces the time required to deploy and scale large language models (LLMs) for inference. We discussed how this innovation addresses one of the major… Read More »Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 Melanie Li AWS Machine Learning Blog

​[[{“value”:” Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent process by clipping individual gradients to a fixed norm and adding noise to the aggregated gradients of… Read More »Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD) Sana Hassan Artificial Intelligence Category – MarkTechPost

​Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show… Read More »Towards Time-Series Reasoning with LLMs Apple Machine Learning Research