Remove 2023 Remove APIs Remove Scripts
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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time. On the other hand, generative artificial intelligence (AI) models can learn these templates and produce coherent scripts when fed with quarterly financial data.

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Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. Solution overview The solution comprises two main steps: Generate synthetic data using the Amazon Bedrock InvokeModel API.

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning

Dataset collection We followed the methodology outlined in the PMC-Llama paper [6] to assemble our dataset, which includes PubMed papers sourced from the Semantic Scholar API and various medical texts cited within the paper, culminating in a comprehensive collection of 88 billion tokens. arXiv preprint arXiv:2308.04014 (2023).

APIs 124
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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. Test the code using the native inference API for Anthropics Claude The following code uses the native inference API to send a text message to Anthropics Claude. client = boto3.client("bedrock-runtime",

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Open source observability for AWS Inferentia nodes within Amazon EKS clusters

AWS Machine Learning

companion script (both commands are part of the container): neuron-monitor | neuron-monitor-prometheus.py --port The command uses the following components: neuron-monitor collects metrics and stats from the Neuron applications running on the system and streams the collected data to stdout in JSON format neuron-monitor-prometheus.py

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

AWS Machine Learning

Since the inception of AWS GenAIIC in May 2023, we have witnessed high customer demand for chatbots that can extract information and generate insights from massive and often heterogeneous knowledge bases. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value.

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Drive hyper-personalized customer experiences with Amazon Personalize and generative AI

AWS Machine Learning

Gartner predicts that “by 2026, more than 80% of enterprises will have used generative AI APIs or models, or deployed generative AI-enabled applications in production environments, up from less than 5% in 2023.” However, scripting appealing subject lines can often be tedious and time-consuming.