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Maximize your Amazon Translate architecture using strategic caching layers

Maximize your Amazon Translate architecture using strategic caching layers

Amazon Translate is a neural machine translation service that delivers fast, high quality, affordable, and customizable language translation. Amazon Translate supports 75 languages and 5,550 language pairs. For the latest list, see the Amazon Translate Developer Guide. A key benefit of Amazon Translate is its speed and scalability. It can translate a large body of …

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Deploy a Slack gateway for Amazon Bedrock

Deploy a Slack gateway for Amazon Bedrock

In today’s fast-paced digital world, streamlining workflows and boosting productivity are paramount. That’s why we’re thrilled to share an exciting integration that will take your team’s collaboration to new heights. Get ready to unlock the power of generative artificial intelligence (AI) and bring it directly into your Slack workspace. Imagine the possibilities: Quick and efficient …

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Improving air quality with generative AI

Improving air quality with generative AI

As of this writing, Ghana ranks as the 27th most polluted country in the world, facing significant challenges due to air pollution. Recognizing the crucial role of air quality monitoring, many African countries, including Ghana, are adopting low-cost air quality sensors. The Sensor Evaluation and Training Centre for West Africa (Afri-SET), aims to use technology …

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Use zero-shot large language models on Amazon Bedrock for custom named entity recognition

Use zero-shot large language models on Amazon Bedrock for custom named entity recognition

Name entity recognition (NER) is the process of extracting information of interest, called entities, from structured or unstructured text. Manually identifying all mentions of specific types of information in documents is extremely time-consuming and labor-intensive. Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or …

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Safeguard a generative AI travel agent with prompt engineering and Guardrails for Amazon Bedrock

Safeguard a generative AI travel agent with prompt engineering and Guardrails for Amazon Bedrock

In the rapidly evolving digital landscape, travel companies are exploring innovative approaches to enhance customer experiences. One promising solution is the integration of generative artificial intelligence (AI) to create virtual travel agents. These AI-powered assistants use large language models (LLMs) to engage in natural language conversations, providing personalized recommendations, answering queries, and guiding customers through …

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Streamline financial workflows with generative AI for email automation

Streamline financial workflows with generative AI for email automation

Many companies across all industries still rely on laborious, error-prone, manual procedures to handle documents, especially those that are sent to them by email. Despite the availability of technology that can digitize and automate document workflows through intelligent automation, businesses still mostly rely on labor-intensive manual document processing. This represents a major opportunity for businesses …

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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

This post is co-written with Shamik Ray, Srivyshnav K S, Jagmohan Dhiman and Soumya Kundu from Twilio. Today’s leading companies trust Twilio’s Customer Engagement Platform (CEP) to build direct, personalized relationships with their customers everywhere in the world. Twilio enables companies to use communications and data to add intelligence and security to every step of …

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Accelerate deep learning training and simplify orchestration with AWS Trainium and AWS Batch

Accelerate deep learning training and simplify orchestration with AWS Trainium and AWS Batch

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, …

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