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.Lee mas.
Este recurso es ofrecido por un socio afiliado. Si paga por la capacitación, podemos ganar una comisión para respaldar este sitio.
Las técnicas y herramientas cubiertas en Optimize ML Models and Deploy Human-in-the-Loop Pipelines son muy similares a los requisitos que se encuentran en los anuncios de trabajo de Científico de datos.
Optimize ML Models and Deploy Human-in-the-Loop Pipelines is a part of uno structured learning path.
3 Courses
3 Months
Practical Data Science
