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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 115
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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning

To further amplify the efficiency of our CPT process, we collaborated with the Trainium team, using their cutting-edge technology to enhance a Llama 2 [4] model using a PubMed dataset [2] comprising 88 billion tokens. Now you can launch a training job to submit a model training script as a slurm job. Shamane Siri Ph.D.

APIs 102
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Introducing an image-to-speech Generative AI application using Amazon SageMaker and Hugging Face

AWS Machine Learning

At the 2022 AWS re:Invent conference in Las Vegas, we demonstrated “Describe for Me” at the AWS Builders’ Fair, a website which helps the visually impaired understand images through image caption, facial recognition, and text-to-speech, a technology we refer to as “Image to Speech.”

APIs 90
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Enhancing AWS intelligent document processing with generative AI

AWS Machine Learning

AWS intelligent document processing (IDP), with AI services such as Amazon Textract , allows you to take advantage of industry-leading machine learning (ML) technology to quickly and accurately process data from any scanned document or image. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale.

APIs 76
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Lessons for Success from Cloud Communications Providers

Jon Arnold

Conversely, with everyone using comparable technology, there’s a sameness to most of the offerings, so to stand out, you somehow need a point of difference, or a niche that you can defend. Each of the following approaches represent distinct opportunities for cloud providers to chart their own course with communications technologies.

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Zero-shot and few-shot prompting for the BloomZ 176B foundation model with the simplified Amazon SageMaker JumpStart SDK

AWS Machine Learning

Answer: 2021 ### Context: NLP Cloud developed their API by mid-2020 and they added many pre-trained open-source models since then. Answer: API ### Context: All plans can be stopped anytime. The model URI, which contains the inference script, and the URI of the Docker container are obtained through the SageMaker SDK.