Machine Learning (ML) is generally being implemented in varying stages in businesses all over, however seamlessly integrating ML workflows into existing infrastructure can be somewhat challenging. In this webinar you will see examples on how to get control of and manage your own pipeline from trained model to production using different AWS services and DevOps tools.
You will be shown examples using AWS services, hereunder:
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How to set up a self service pipeline using PaaS services
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How you can set up CI/CD with Sagemaker
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Benefits of using AWS Code Pipeline, like testing of more robust models
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Continuous validation of accuracy of models in production
The webinar will be hosted by Mia Ryan and AWS Solution Architect Yngve Sandal from Redpill Linpro.
There will be 60 minutes of presentation with demo, following 30 minutes of QA.