/********************************************************************************************************************* * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * * * * Permission is hereby granted, free of charge, to any person obtaining a copy of * * this software and associated documentation files (the "Software"), to deal in * * the Software without restriction, including without limitation the rights to * * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of * * the Software, and to permit persons to whom the Software is furnished to do so. * * * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS * * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR * * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER * * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN * * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. * *********************************************************************************************************************/ import { FunctionComponent } from 'react' import { Box, Container, Link } from 'aws-northstar' import { useAuthContext } from '../../contexts/AuthenticatedUserContext' const HomePage: FunctionComponent = () => { const authContext = useAuthContext() return ( <>
  1. Create asset entries and upload CSVs (or use the  asset import tool )
  2. Create a model training template and set all the parameters
  3. Create a model training execution and select the template to use
  4. Trigger model training with the Start model training button
  5. Lambda function will call startPipelineExecution with the right parameters
  6. Processing step performs the feature engineering step, stores features/test/training data in S3
  7. Training step trains the model
  8. Model gets created and registered with Sagemaker
  9. Create model endpoint (and config) from the UI using the Create endpoint button
  10. Run inference against the trained model with parameters set on the UI
  11. Analyze output from inference on a chart in the UI
  12. Delete model endpoint manually or leave it{' '} and it will be automatically cleaned up after 60 minutes

This project has been created by the ASEAN Prototyping Team  |   Code   |   Issues

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

) } export default HomePage