Optimize ML Models and Deploy Human-in-the-Loop Pipelines
This is the third course of the Practical Data Science Specialization.
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This course teaches a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence.
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After tuning a text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting.
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Set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth.
