Build, Train, and Deploy ML Pipelines using BERT from Coursera

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.

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Develops skills critical for data-focused developers, scientists, and analysts

Well-suited for those with experience in Python and SQL programming

Emphasizes practical applications of data science in industry

Collaboration with Amazon SageMaker provides industry-relevant experience

Covers end-to-end machine learning workflows, enhancing learner employability

May require prior knowledge of natural language processing concepts

Reviews summary

Sagemaker pipelines with bert

This course teaches students how to build, train, and deploy machine learning pipelines using Amazon SageMaker Pipelines and Hugging Face's BERT algorithm. It is part of the Practical Data Science Specialization, which is designed for data-focused professionals who want to learn how to use AWS cloud services for data science projects. Overall, the course is well-received by students, with many praising the hands-on labs and practical information it provides. However, some students have expressed concerns about the difficulty of the labs and the amount of time required to complete them.

The course content is clear and easy to understand, but it may not be challenging enough for some students.

"I only got a rough idea of the MLOps."

"I suggest the lab exercises be harder for better learning experiences."

"This is NOT a course about BERT, it's a course about Amazon SageMaker ML Ops."

Content is directly applicable to industry use cases.

"Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!"

"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"

Practical, hands-on labs reinforce learning.

"Very Hands On Practical Information for the Industry"

"Very hands-on AWS BERT labs!"

"Detailed code walk through explaining the code would have been helpful similar how it was done in Tensorflow In Practice Specalization"

Some students have experienced technical issues with the labs.

"I was a little disappointed in the courses in this specialization - the issue is that a large part of the coding was already done."

"The majority of the graded material requires little to no thought -- copying and pasting variable names without understanding any of the mechanics that go into it would be sufficient to pass with 100%."

"Many of the assignments contain errors that must be fixed before they can be completed."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Build, Train, and Deploy ML Pipelines using BERT with these activities:

Organize your course materials

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Improve your ability to locate and review important course materials.

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Create a system for organizing notes, assignments, and readings
Review your materials regularly

Review machine learning concepts

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Strengthen your foundation in machine learning concepts to better understand the course material.

Browse courses on Machine Learning

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Review basic machine learning algorithms
Practice implementing machine learning models
Explore different machine learning libraries

Review prerequisite coding language

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Refresh your understanding of the Python and SQL programming languages to ensure you have a solid foundation for the course.

Browse courses on Python

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Review basic Python syntax and data structures
Practice writing and executing simple Python code
Review basic SQL queries and database concepts

Six other activities

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Follow tutorials on Amazon SageMaker Pipelines

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Supplement your understanding of Amazon SageMaker Pipelines by following guided tutorials.

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Find and access tutorials on Amazon SageMaker Pipelines
Follow the instructions in the tutorials
Experiment with different pipeline configurations

Practice using the Hugging Face library

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Gain proficiency in using the Hugging Face library for natural language processing tasks.

Browse courses on Hugging Face

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Install the Hugging Face library
Load and explore a dataset using Hugging Face
Fine-tune a pre-trained model using Hugging Face

Classify Texts Using Hugging Face's BERT

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Practice classifying text data using Hugging Face's BERT to enhance your understanding of text classification techniques.

Browse courses on Text Classification

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Load the necessary libraries and data.
Tokenize and pre-process the text data.
Train a text classification model using Hugging Face's BERT.
Evaluate the model's performance on a test dataset.

Build a language processing pipeline using Hugging Face

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Reinforce your understanding of building end-to-end machine learning pipelines for natural language processing tasks by creating your own pipeline using Hugging Face.

Browse courses on Natural Language Processing

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Choose a dataset for your pipeline
Create a Hugging Face pre-trained model
Train and evaluate your model
Deploy your model to Amazon SageMaker Pipelines

Build an End-to-End ML Pipeline for Text Classification

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Develop a complete ML pipeline to automate the text classification task, reinforcing your mastery of end-to-end ML workflows.

Browse courses on Machine Learning Pipeline

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Define the pipeline components.
Connect the pipeline components.
Deploy the pipeline to Amazon SageMaker.
Monitor and evaluate the deployed pipeline.

Answer questions in the course discussion forums

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Enhance your understanding of the course material by helping others and clarifying concepts.

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Monitor the course discussion forums
Identify questions that you can answer
Provide clear and concise answers

Career center

Learners who complete Build, Train, and Deploy ML Pipelines using BERT will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Natural Language Processing Engineers develop and maintain the natural language processing models that power a wide range of applications, from chatbots to machine translation systems. This course will help you build a strong foundation in the fundamentals of natural language processing, including text preprocessing, feature engineering, and model evaluation. You will also learn how to use Hugging Face, a library that provides state-of-the-art natural language processing models. With the skills you gain from this course, you will be well-prepared for a career as a Natural Language Processing Engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Data Scientist

Data Scientists use their knowledge of machine learning, statistics, and data analysis to extract insights from data. This course will help you build a strong foundation in the fundamentals of data science, including data preprocessing, feature engineering, and model evaluation. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Data Scientist.

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Machine Learning Engineer

Machine Learning Engineers develop and maintain the machine learning models that power a wide range of applications, from self-driving cars to fraud detection systems. This course will help you build a strong foundation in the fundamentals of machine learning, including data preprocessing, model training, and model evaluation. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Machine Learning Engineer.

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Cloud Architect

Cloud Architects design and manage cloud-based infrastructure. This course will help you build a strong foundation in the fundamentals of cloud computing, including cloud architecture, cloud security, and cloud cost management. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Cloud Architect.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course will help you build a strong foundation in the fundamentals of software engineering, including software design, software development, and software testing. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Software Engineer.

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Data Analyst

Data Analysts use their knowledge of data analysis to extract insights from data. This course will help you build a strong foundation in the fundamentals of data analysis, including data preprocessing, data visualization, and data modeling. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Data Analyst.

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Product Manager

Product Managers are responsible for the development and management of products. This course will help you build a strong foundation in the fundamentals of product management, including product planning, product development, and product marketing. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Product Manager.

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Project Manager

Project Managers are responsible for the planning, execution, and control of projects. This course will help you build a strong foundation in the fundamentals of project management, including project planning, project scheduling, and project control. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Project Manager.

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Business Analyst

Business Analysts use their knowledge of business and data analysis to solve business problems. This course will help you build a strong foundation in the fundamentals of business analysis, including business process analysis, data analysis, and financial analysis. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Business Analyst.

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Museum curator

Museum Curators manage and interpret museum collections. This course will help you build a strong foundation in the fundamentals of museum studies, including museum curation, museum education, and museum management. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Museum Curator.

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Teacher

Teachers educate students in a variety of subjects. This course will help you build a strong foundation in the fundamentals of teaching, including lesson planning, classroom management, and assessment. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Teacher.

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Historian

Historians research and write about the past. This course will help you build a strong foundation in the fundamentals of history, including historical research, historical writing, and historical interpretation. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Historian.

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Archivist

Archivists preserve and manage historical records. This course will help you build a strong foundation in the fundamentals of archival science, including archival theory, archival methods, and archival preservation. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as an Archivist.

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Librarian

Librarians organize and maintain libraries. This course will help you build a strong foundation in the fundamentals of library science, including library organization, library management, and library services. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Librarian.

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Technical Writer

Technical Writers create and maintain technical documentation. This course will help you build a strong foundation in the fundamentals of technical writing, including technical writing principles, technical writing tools, and technical writing style. You will also learn how to use Amazon SageMaker, a cloud-based platform that makes it easy to build, train, and deploy machine learning models. With the skills you gain from this course, you will be well-prepared for a career as a Technical Writer.

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