Build, Train, and Deploy ML Pipelines using BERT
- Offered byCoursera
Build, Train, and Deploy ML Pipelines using BERT at Coursera Overview
Duration | 10 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Build, Train, and Deploy ML Pipelines using BERT at Coursera Highlights
- Reset deadlines in accordance to your schedule.
- Earn a Certificate upon completion
- Start instantly and learn at your own schedule.
Build, Train, and Deploy ML Pipelines using BERT at Coursera Course details
- 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.
- Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources.
- One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost.
- The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.
Build, Train, and Deploy ML Pipelines using BERT at Coursera Curriculum
Week 1: Feature Engineering and Feature Store
Course 2 overview
Week 1 Outline
Introduction to Feature Engineering
Feature Engineering Steps
Feature Engineering Pipeline
BERT: Bidirectional Encoder Representations from Transformers
BERT: Example
Feature Engineering: At scale with Amazon SageMaker Processing Jobs
Feature Store
Amazon SageMaker Feature Store
Week 1 Summary
Have questions? Meet us on Discourse!
Week 1: optional references
Week 1 quiz
Week 2: Train, Debug, and Profile a Machine Learning Model
Week 2 Introduction
Train and Debug a Custom Machine Learning Model
Pre-trained models
Pre-trained BERT models
Train a custom model with Amazon SageMaker
Debug and profile models
Debug and Profile Models with Amazon SageMaker Debugger
Week 2 Summary
Week 2: optional references
Week 2 quiz
Week 3: Deploy End-To-End Machine Learning pipelines
Week 3 Outline
Machine Learning Operations (MLOps) Overview
Creating Machine Learning Pipelines
Model Lineage & Artifact Tracking
Machine Learning Pipelines with Amazon SageMaker Pipelines
Machine Learning Pipelines with Amazon SageMaker Projects
Amazon SageMaker Projects Demo
Week 3 Summary
Week 3: optional references
Course 2 Optional References
Acknowledgements
Week 3 quiz
Build, Train, and Deploy ML Pipelines using BERT at Coursera Faculty details
Build, Train, and Deploy ML Pipelines using BERT at Coursera Admission Process
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