Introducing Genomics Tertiary Analysis and Machine Learning using Amazon SageMaker
Genomics Tertiary Analysis and Machine Learning Using Amazon SageMaker is a new AWS Solutions Implementation that creates a scalable environment in AWS to develop machine learning models using genomics data, generate predictions, and evaluate model performance.
Genomics Tertiary Analysis and Machine Learning Using Amazon SageMaker is a new AWS Solutions Implementation that creates a scalable environment in AWS to develop machine learning models using genomics data, generate predictions, and evaluate model performance.
This solution demonstrates how to 1) automate the preparation of a genomics machine learning training dataset, 2) develop genomics machine learning model training and deployment pipelines and, 3) generate predictions and evaluate model performance using test data. The solution uses AWS CloudFormation to automate its deployment in the AWS Cloud, and it includes continuous integration and continuous delivery (CI/CD) using AWS CodeCommit source code repositories and AWS CodePipeline for building and deploying updates to the data preparation jobs and solution notebooks. It fully leverages infrastructure as code principles and best practices that enable you to rapidly evolve the solution.
To learn more about Genomics Tertiary Analysis and Machine Learning Using Amazon SageMaker, see the AWS Solutions Implementation webpage.
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