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.
This course is part of the Practical Data Science on the AWS Cloud Specialization
Offered By
Practical Data Science on the AWS Cloud SpecializationDeepLearning.AIAbout this Course
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
Store and manage machine learning features using a feature store
Debug, profile, tune and evaluate models while tracking data lineage and model artifacts
Skills you will gain
- 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
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
Syllabus - What you will learn from this course
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
Reviews
- 5 stars70.73%
- 4 stars17.07%
- 3 stars8.94%
- 2 stars1.62%
- 1 star1.62%
TOP REVIEWS FROM BUILD, TRAIN, AND DEPLOY ML PIPELINES USING BERT
Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!
Week 3 lab gave twice error 'Failed' and 3rd time it went without an issue. This was quite frustrating. Overall, good class. Thx.
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
About the Practical Data Science on the AWS Cloud Specialization
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