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.
이 강좌는 Practical Data Science on the AWS Cloud 특화 과정의 일부입니다.
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Practical Data Science on the AWS Cloud 특화 과정deeplearning.ai이 강좌에 대하여
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
직원에게 수요가 높은 기술을 교육하면 회사가 이점을 얻을 수 있습니까?
비즈니스를 위한 Coursera 경험해 보기배울 내용
Store and manage machine learning features using a feature store
Debug, profile, tune and evaluate models while tracking data lineage and model artifacts
귀하가 습득할 기술
- 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
직원에게 수요가 높은 기술을 교육하면 회사가 이점을 얻을 수 있습니까?
비즈니스를 위한 Coursera 경험해 보기
강의 계획표 - 이 강좌에서 배울 내용
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
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- 1 star0.82%
BUILD, TRAIN, AND DEPLOY ML PIPELINES USING BERT의 최상위 리뷰
Very hands-on AWS BERT labs! Expecting more labs coming...
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
Very Hands On Practical Information for the Industry
Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!
Practical Data Science on the AWS Cloud 특화 과정 정보
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