Category: Intermediate (200)

In this post, we explore a comprehensive solution for addressing the challenges of securing a virtual travel agent powered by generative AI. We provide an end-to-end example and its accompanying code to demonstrate how to implement prompt engineering techniques, content moderation, and various guardrails to make sure the assistant operates within predefined boundaries by relying on Guardrails for Amazon Bedrock. Additionally, we delve into monitoring strategies to track the activation of these safeguards, enabling proactive identification and mitigation of potential issues.

In large language model (LLM) training, effective orchestration and compute resource management poses a significant challenge. Automation of resource provisioning, scaling, and workflow management is vital for optimizing resource usage and streamlining complex workflows, thereby achieving efficient deep learning training processes. Simplified orchestration enables researchers and practitioners to focus more on model experimentation, hyperparameter tuning, […]

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability. In this post, we discuss an approach that uses the […]

Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and then use those patterns to estimate likely values about unseen periods […]

Amazon Q Developer uses generative artificial intelligence (AI) to deliver state-of-the-art accuracy for all developers, taking first place on the leaderboard for SWE-bench, a dataset that tests a system’s ability to automatically resolve GitHub issues. This post describes how to get started with the software development agent, gives an overview of how the agent works, and discusses its performance on public benchmarks. We also delve into the process of getting started with the Amazon Q Developer Agent and give an overview of the underlying mechanisms that make it a state-of-the-art feature development agent.

Starting with the AWS Neuron 2.18 release, you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs […]

Phishing is the process of attempting to acquire sensitive information such as usernames, passwords and credit card details by masquerading as a trustworthy entity using email, telephone or text messages. There are many types of phishing based on the mode of communication and targeted victims. In an Email phishing attempt, an email is sent as […]

In today’s fast-paced corporate landscape, employee mental health has become a crucial aspect that organizations can no longer overlook. Many companies recognize that their greatest asset lies in their dedicated workforce, and each employee plays a vital role in collective success. As such, promoting employee well-being by creating a safe, inclusive, and supportive environment is […]

Organizations across media and entertainment, advertising, social media, education, and other sectors require efficient solutions to extract information from videos and apply flexible evaluations based on their policies. Generative artificial intelligence (AI) has unlocked fresh opportunities for these use cases. In this post, we introduce the Media Analysis and Policy Evaluation solution, which uses AWS […]

Mixture of Experts (MoE) architectures for large language models (LLMs) have recently gained popularity due to their ability to increase model capacity and computational efficiency compared to fully dense models. By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per […]