Accelerating MLOps at Bayer Crop Science with Kubeflow Pipelines and Amazon SageMaker

By Amazon Web Services - 2021-01-05

Description

This is a guest post by the data science team at Bayer Crop Science.  Farmers have always collected and evaluated a large amount of data with each growing season: seeds planted, crop protection inputs ...

Summary

  • Farmers have always collected and evaluated a large amount of data with each growing season: An S3 bucket is also created to store trained model artifacts and any data required by the model during inference or training.
  • Generating a model artifact The SageMaker Create Model KubeFlow Pipelines component generates a .tar.gz file containing the model configuration and trained parameters for downstream use.
  • Performing Bayer-specific postprocessing Finally, the pipeline generates an Amazon API Gateway deployment and other Bayer-specific resources required for other applications within the Bayer network to use the model.

 

Topics

  1. Backend (0.42)
  2. Database (0.13)
  3. NLP (0.07)

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