Build, Train, and Deploy ML Pipelines using BERT

This course is part of the Practical Data Science Specialization

4.6

stars

15 ratings

Antje Barth +3 more instructors

1,833 already enrolled

Offered By

DeepLearning.AI

Amazon Web Services

Practical Data Science SpecializationDeepLearning.AI

About this Course

42,740 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Practical Data Science Specialization

Advanced Level

Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses

Approx. 11 hours to complete

English

Subtitles: English

What 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 MLOpsModel Training and Deployment with BERTModel Debugging and EvaluationFeature engineering and feature storeArtifact and lineage tracking

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Practical Data Science Specialization

Advanced Level

Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses

Approx. 11 hours to complete

English

Subtitles: English

Instructors

Instructor rating

5/5 (6 Ratings)

Offered by

DeepLearning.AI

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Amazon Web Services

Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 90 fully featured services for compute, storage, networking, database, analytics, application services, deployment, management, developer, mobile, Internet of Things (IoT), Artificial Intelligence, security, hybrid and enterprise applications, from 44 Availability Zones across 16 geographic regions. AWS services are trusted by millions of active customers around the world — including the fastest-growing startups, largest enterprises, and leading government agencies — to power their infrastructure, make them more agile, and lower costs.

Coursera and AWS have been partners since 2017 providing learners and enterprises globally, the skills they need to succeed. Coursera builds on AWS servers to scale with student demand with confidence around capacity and elasticity and in partnership with AWS. In 2019, Coursera achieved Advanced Tier Partner status and further extended the partnership with AWS Educate, AWS EdStart and AWS Academy collaborations. Coursera's been able to make cloud skills more accessible with 8 AWS courses on the Coursera platform featuring top subject matter experts and the portfolio continues to grow. To learn more about AWS, visit https://aws.amazon.com.

Syllabus - What you will learn from this course

Week

Week 1

3 hours to complete

Week 1: Feature Engineering and Feature Store

Transform a raw text dataset into machine learning features and store features in a feature store.

3 hours to complete

11 videos (Total 40 min), 2 readings, 2 quizzes

11 videos

Course 2 overview4m

Week 1 Outline1m

Introduction to Feature Engineering1m

Feature Engineering Steps5m

Feature Engineering Pipeline3m

BERT: Bidirectional Encoder Representations from Transformers4m

BERT: Example4m

Feature Engineering: At scale with Amazon SageMaker Processing Jobs6m

Feature Store2m

Amazon SageMaker Feature Store5m

Week 1 Summary1m

2 readings

Connect with your Mentors and Fellow Learners on Discourse!10m

Week 1: optional references10m

1 practice exercise

Week 1 quiz20m

Week

Week 2

3 hours to complete

Week 2: Train, Debug, and Profile a Machine Learning Model

Fine-tune, debug, and profile a pre-trained BERT model.

3 hours to complete

8 videos (Total 38 min), 1 reading, 2 quizzes

Week

Week 3

4 hours to complete

Week 3: Deploy End-To-End Machine Learning pipelines

Orchestrate ML workflows and track model lineage and artifacts in an end-to-end machine learning pipeline.

4 hours to complete

8 videos (Total 63 min), 4 readings, 2 quizzes

8 videos

Week 3 Outline2m

Machine Learning Operations (MLOps) Overview15m

Creating Machine Learning Pipelines10m

Model Lineage & Artifact Tracking5m

Machine Learning Pipelines with Amazon SageMaker Pipelines18m

Machine Learning Pipelines with Amazon SageMaker Projects1m

Amazon SageMaker Projects Demo6m

Week 3 Summary2m

4 readings

Week 3: optional references10m

References10m

Acknowledgements10m

Become a Deeplearning.AI Mentor/Tester10m

1 practice exercise

Week 3 quiz20m

About the Practical Data Science Specialization

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It 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 who 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. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Build, Train, and Deploy ML Pipelines using BERT