In the third course of the Practical Data Science Specialization, you will learn 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.
After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will 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. Lastly, you will 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.Read more.
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The techniques and tools covered in Optimize ML Models and Deploy Human-in-the-Loop Pipelines are most similar to the requirements found in Data Scientist job advertisements.
Optimize ML Models and Deploy Human-in-the-Loop Pipelines is a part of one structured learning path.