​[[{“value”:” In AI, a key challenge lies in improving the efficiency of systems that process unstructured datasets to extract valuable insights. This involves enhancing retrieval-augmented generation (RAG) tools, combining traditional search and AI-driven analysis to answer localized and overarching queries. These advancements address diverse questions,… Read More »Microsoft AI Introduces LazyGraphRAG: A New AI Approach to Graph-Enabled RAG that Needs No Prior Summarization of Source Data Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have transformed the development of agent-based systems for good. However, managing memory in these systems remains a complex challenge. Memory mechanisms enable agents to maintain context, recall important information, and interact more naturally over extended periods. While many frameworks assume… Read More »Exploring Memory Options for Agent-Based Systems: A Comprehensive Overview Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent years, there has been a growing demand for machine learning models capable of handling visual and language tasks effectively, without relying on large, cumbersome infrastructure. The challenge lies in balancing performance with resource requirements, particularly for devices like laptops, consumer GPUs, or… Read More »Hugging Face Releases SmolVLM: A 2B Parameter Vision-Language Model for On-Device Inference Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Thousands of companies worldwide use Salesforce to manage their sales, marketing, customer service, and other business operations. The Salesforce cloud-based platform centralizes customer information and interactions across the organization, providing sales reps, marketers, and support agents with a unified 360-degree view of each customer.… Read More »Unleash your Salesforce data using the Amazon Q Salesforce Online connector Mehdy Haghy AWS Machine Learning Blog

​[[{“value”:” Hallucinations in large language models (LLMs) refer to the phenomenon where the LLM generates an output that is plausible but factually incorrect or made-up. This can occur when the model’s training data lacks the necessary information or when the model attempts to generate coherent… Read More »Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents Shayan Ray AWS Machine Learning Blog

​[[{“value”:” With the rise of large language models (LLMs) like Meta Llama 3.1, there is an increasing need for scalable, reliable, and cost-effective solutions to deploy and serve these models. AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS),… Read More »Deploy Meta Llama 3.1-8B on AWS Inferentia using Amazon EKS and vLLM Maurits de Groot AWS Machine Learning Blog

​[[{“value”:” The use of large language models (LLMs) and generative AI has exploded over the last year. With the release of powerful publicly available foundation models, tools for training, fine tuning and hosting your own LLM have also become democratized. Using vLLM on AWS Trainium… Read More »Serving LLMs using vLLM and Amazon EC2 instances with AWS AI chips Omri Shiv AWS Machine Learning Blog

​[[{“value”:” This post is co-written with Adarsh Kyadige and Salma Taoufiq from Sophos.  As a leader in cutting-edge cybersecurity, Sophos is dedicated to safeguarding over 500,000 organizations and millions of customers across more than 150 countries. By harnessing the power of threat intelligence, machine learning… Read More »Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker Benoît de Patoul AWS Machine Learning Blog

​[[{“value”:” This post is co-written with Curtis Maher and Anjali Thatte from Datadog.  This post walks you through Datadog’s new integration with AWS Neuron, which helps you monitor your AWS Trainium and AWS Inferentia instances by providing deep observability into resource utilization, model execution performance,… Read More »Enhanced observability for AWS Trainium and AWS Inferentia with Datadog Curtis Maher AWS Machine Learning Blog

​[[{“value”:” Stock technical analysis questions can be as unique as the individual stock analyst themselves. Queries often have multiple technical indicators like Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), and others. Answering these varied questions would mean writing complex business… Read More »Create a virtual stock technical analyst using Amazon Bedrock Agents Bharath Sridharan AWS Machine Learning Blog