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Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 | Amazon Web Services

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 bottlenecks in LLM deployment: the timeContinue Reading

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 | Amazon Web Services

In: Artificial Intelligence

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. As LLMs continue to expand, AIContinue Reading

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale | Amazon Web Services

In: Artificial Intelligence

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. To view this series from the beginning, start with Part 1. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The dataContinue Reading

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2 | Amazon Web Services

In: Artificial Intelligence

In Part 1 of this series, we defined the Retrieval Augmented Generation (RAG) framework to augment large language models (LLMs) with a text-only knowledge base. We gave practical tips, based on hands-on experience with customer use cases, on how to improve text-only RAG solutions, from optimizing the retriever to mitigatingContinue Reading

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1 | Amazon Web Services

In: Artificial Intelligence

The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value. Since the inception of AWSContinue Reading

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2 | Amazon Web Services

In: Artificial Intelligence

In Part 1 of this series, we explored best practices for creating accurate and reliable agents using Amazon Bedrock Agents. Amazon Bedrock Agents help you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to create a plan that decomposes theContinue Reading

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1 | Amazon Web Services

In: Artificial Intelligence

Building intelligent agents that can accurately understand and respond to user queries is a complex undertaking that requires careful planning and execution across multiple stages. Whether you are developing a customer service chatbot or a virtual assistant, there are numerous considerations to keep in mind, from defining the agent’s scopeContinue Reading

Streamline insurance underwriting with generative AI using Amazon Bedrock – Part 1 | Amazon Web Services

In: Artificial Intelligence

Underwriting is a fundamental function within the insurance industry, serving as the foundation for risk assessment and management. Underwriters are responsible for evaluating insurance applications, determining the level of risk associated with each applicant, and making decisions on whether to accept or reject the application based on the insurer’s guidelinesContinue Reading

Governing the ML lifecycle at scale, Part 2: Multi-account foundations | Amazon Web Services

In: Artificial Intelligence

Your multi-account strategy is the core of your foundational environment on AWS. Design decisions around your multi-account environment are critical for operating securely at scale. Grouping your workloads strategically into multiple AWS accounts enables you to apply different controls across workloads, track cost and usage, reduce the impact of accountContinue Reading

CNN slashes 100 jobs as part of newsroom structure realignment (NASDAQ:WBD)

In: Finance

Anna Moneymaker/Getty Images News CNN will slash 100 positions and undergo structural changes in a move aimed at consolidating news operations and strengthening its digital business, the namesake news agency reported on Wednesday, citing an internal memo. The report claimed CNN Worldwide CEO Mark Thompson told staff in the memoContinue Reading

part (Page 2)

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 | Amazon Web Services

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 | Amazon Web Services

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale | Amazon Web Services

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2 | Amazon Web Services

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1 | Amazon Web Services

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2 | Amazon Web Services

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1 | Amazon Web Services

Streamline insurance underwriting with generative AI using Amazon Bedrock – Part 1 | Amazon Web Services

Governing the ML lifecycle at scale, Part 2: Multi-account foundations | Amazon Web Services

CNN slashes 100 jobs as part of newsroom structure realignment (NASDAQ:WBD)

Scientists just solved a 40-year-old mystery about quasicrystals

This quantum sensor tracks 3D movement without GPS