开始时间: 04/22/2022 持续时间: Unknown
所在平台: CourseraArchive 课程类别: 其他类别 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/archive/ml-pipelines-bert
课程评论:没有评论
In the second course of the Practical Data Science Specialization, you will learn to automate a natural language processing task by building an end-to-end machine learning pipeline using Hugging Face’s highly-optimized implementation of the state-of-the-art BERT algorithm with Amazon SageMaker Pipelines. Your pipeline will first transform the dataset into BERT-readable features and store the features in the Amazon SageMaker Feature Store. It will then fine-tune a text classification model to the dataset using a Hugging Face pre-trained model, which has learned to understand the human language from millions of Wikipedia documents. Finally, your pipeline will evaluate the model’s accuracy and only deploy the model if the accuracy exceeds a given threshold.
Week 1: Feature Engineering and Feature Store
Week 2: Train, Debug, and Profile a Machine Learning Model
Week 3: Deploy End-To-End Machine Learning pipelines
In the second course of the Practical Data Science Specialization, you will learn to automate a natural language process