AWS Machine Learning Blog

NOVEMBER 22, 2023

Building a production-ready solution in AWS involves a series of trade-offs between resources, time, customer expectation, and business outcome. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS.

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AWS Machine Learning Blog

DECEMBER 13, 2023

The built-in project templates provided by Amazon SageMaker include integration with some of third-party tools, such as Jenkins for orchestration and GitHub for source control, and several utilize AWS native CI/CD tools such as AWS CodeCommit , AWS CodePipeline , and AWS CodeBuild. all implemented via CloudFormation.

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AWS Machine Learning Blog

AUGUST 14, 2023

Amazon OpenSearch OpenSearch Service is a fully managed service that makes it simple to deploy, scale, and operate OpenSearch in the AWS Cloud. as our example data to perform retrieval augmented question answering on. Here, we walk through the steps for indexing to an OpenSearch service deployed on AWS.

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AWS Machine Learning Blog

MARCH 28, 2024

For more information on Mixtral-8x7B Instruct on AWS, refer to Mixtral-8x7B is now available in Amazon SageMaker JumpStart. Before you get started with the solution, create an AWS account. This identity is called the AWS account root user. For more detailed steps to prepare the data, refer to the GitHub repo.

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AWS Machine Learning Blog

AUGUST 16, 2023

It simplifies the development and maintenance of ML models by providing a centralized platform to orchestrate tasks such as data preparation, model training, tuning and validation. About the Authors Pranav Murthy is an AI/ML Specialist Solutions Architect at AWS. In his free time, he enjoys playing chess and traveling.

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AWS Machine Learning Blog

SEPTEMBER 12, 2023

By implementing a modern natural language processing (NLP) model, the response process has been shaped much more efficiently, and waiting time for clients has been reduced tremendously. To facilitate our ML lifecycle process, we decided to adopt SageMaker to build, deploy, serve, and monitor our models.

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