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.