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Build, Train, and Deploy ML Pipelines using BERT

Approx. 10 hours to complete

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Course Summary

This course teaches you how to build Machine Learning pipelines with BERT, a powerful language representation model. You'll learn how to preprocess data, train and evaluate models, and deploy them for production.

Key Learning Points

Understand the basics of Machine Learning pipelines and BERT language representation model
Learn how to preprocess data for Machine Learning
Train and evaluate Machine Learning models using BERT
Deploy Machine Learning models for production

Learning Outcomes

Learn how to build Machine Learning pipelines with BERT
Understand how to preprocess data for Machine Learning
Be able to train, evaluate, and deploy Machine Learning models for production

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with Machine Learning concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Hands-on exercises

Similar Courses

Machine Learning
Natural Language Processing

Related Education Paths

Notable People in This Field

Co-founder of Coursera

Related Books

Description

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.

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. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

Knowledge

Store and manage machine learning features using a feature store
Debug, profile, tune and evaluate models while tracking data lineage and model artifacts

Outline

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

Summary of User Reviews

ML Pipelines with BERT is a highly recommended course for anyone interested in natural language processing and machine learning. Many users appreciated the in-depth coverage of BERT, a powerful language model that has revolutionized the field of NLP.

Key Aspect Users Liked About This Course

In-depth coverage of BERT

Pros from User Reviews

Comprehensive coverage of BERT and ML pipelines
Great for beginners and advanced learners
Hands-on exercises and real-world examples
Engaging and knowledgeable instructors
Useful resources and references provided

Cons from User Reviews

Some sections may be too technical for beginners
Lack of focus on other NLP models besides BERT
No certificate of completion offered
Limited peer interaction and feedback
Occasional technical issues with the platform

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