Developing a high-quality ML model involves many steps. We typically start with exploring and preparing our data. We experiment with different algorithms and parameters. We spend time training and tuning our model until the model meets our quality metrics, and is ready to be deployed into production.
Orchestrating and automating workflows across each step of this model development process can take months of coding. In this session, we show you how to create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.
Resources:
aws.amazon.com/sagemaker/pipelines/
docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html
github.com/aws/amazon-sagemaker-examples/tree/master/sagemaker-pipelines Subscribe to AWS Online Tech Talks On AWS:
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