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.
本课程是 Practical Data Science on the AWS Cloud 专项课程 专项课程的一部分
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Practical Data Science on the AWS Cloud 专项课程deeplearning.ai课程信息
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
对员工进行热门技能培训能否为您的公司带来益处？
体验 Coursera 企业版您将学到的内容有
Store and manage machine learning features using a feature store
Debug, profile, tune and evaluate models while tracking data lineage and model artifacts
您将获得的技能
- ML Pipelines and MLOps
- Model Training and Deployment with BERT
- Model Debugging and Evaluation
- Feature engineering and feature store
- Artifact and lineage tracking
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
对员工进行热门技能培训能否为您的公司带来益处？
体验 Coursera 企业版
授课大纲 - 您将从这门课程中学到什么
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
审阅
- 5 stars71.90%
- 4 stars17.35%
- 3 stars9.09%
- 2 stars0.82%
- 1 star0.82%
来自 BUILD, TRAIN, AND DEPLOY ML PIPELINES USING BERT的热门评论
Very Hands On Practical Information for the Industry
Very hands-on AWS BERT labs! Expecting more labs coming...
Week 3 lab gave twice error 'Failed' and 3rd time it went without an issue. This was quite frustrating. Overall, good class. Thx.
It is one of course with the exact content required for an working professional who is already working with AWS and want to leverage the benefits of sagemaker for their ML deployment tasks
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