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.阅读更多.
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Optimize ML Models and Deploy Human-in-the-Loop Pipelines 中涵盖的技术和工具与 数据科学家 招聘广告中的要求最为相似。
Optimize ML Models and Deploy Human-in-the-Loop Pipelines is a part of 一 structured learning path.
3 Courses
3 Months
Practical Data Science
