We may earn an affiliate commission when you visit our partners.

MLOps Tools

MLflow and Hugging Face

Noah Gift and Alfredo Deza

3.7

Based on 42 ratings

, see reviews

Noah Gift and Alfredo Deza

This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories so that you can store datasets, models, and create live interactive demos.

Read more

This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories so that you can store datasets, models, and create live interactive demos.

By the end of the course, you will be able to apply MLOps concepts like fine-tuning and deploying containerized models to the Cloud. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their programming skills.

Or subscribe to Coursera Plus

And get unlimited access to Coursera

What's inside

Syllabus

Introduction to MLflow

This week, you will learn what MLflow is and how to use it. You’ll install MLflow and perform basic operations like registering runs, models, and artifacts. Then, you’ll create an MLflow project for reproducible results. Finally, you’ll understand how to use a registry with MLflow models and reference artifacts from the API.

Read more

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores foundational concepts of MLOps and machine learning through the prism of two popular, industry standard open source platforms: MLflow and Hugging Face

Taught by Noah Gift and Alfredo Deza, who are recognized for their work in MLOps and machine learning

Builds strong foundational skills in MLOps and machine learning through the concrete example of two open source tools

Develops professional skills and deep expertise in MLOps and machine learning, including fine-tuning, deploying, and containerizing models

Takes a creative approach to teaching MLOps and machine learning by using the lens of two popular, industry standard open source tools

Requires students to come in with some background knowledge in machine learning and programming concepts

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Practical mlops with mlflow and hugging face

According to learners, this course is a largely positive experience, offering a solid introduction to MLOps concepts through practical application of MLflow and Hugging Face. Many highlight the hands-on activities and clear explanations as particularly beneficial, making complex topics accessible. Students appreciate the focus on real-world tools and deployment strategies like containerization and cloud integration (Azure). However, some learners note that a foundational understanding of Python and machine learning is a necessary prerequisite, and occasionally the pace can be quick, requiring diligent engagement. Overall, it serves as a valuable resource for breaking into or advancing in MLOps.

Generally current, but some tools evolve rapidly.

"The core MLOps concepts are timeless, but keep in mind that specific tool versions might update rapidly."

"I appreciated seeing some of the recent updates reflected in the course content, showing continuous improvement."

"Some parts felt a little dated due to the fast pace of tech, but the fundamental principles taught are still very relevant."

Teaches essential skills for deploying models to cloud environments.

"The section on containerizing models with Docker and deploying to Azure was incredibly valuable for my job."

"Learning how to serve models with FastAPI and integrating with Azure made the entire deployment concepts click."

"I appreciate the focus on practical deployment strategies like Hugging Face spaces and cloud integration."

Provides a robust introduction to MLflow and Hugging Face platforms.

"It perfectly introduced me to MLflow's tracking and model registry, which are essential for MLOps workflows."

"The deep dive into Hugging Face repositories and fine-tuning models was incredibly useful for my work."

"This course covers two of the most popular platforms for MLOps, giving me a solid foundation in both."

Offers valuable practical experience with core MLOps tools.

"The hands-on coding and projects are the strongest part of the course for me, really cementing the concepts."

"I loved how practical this course was. It's not just theory; you actually get to use MLflow and Hugging Face effectively."

"The practical exercises and demonstrations helped me apply what I learned immediately to my own projects."

Requires prior knowledge in Python and basic machine learning.

"As a beginner, I found some parts moved very quickly, assuming more prior ML knowledge than I had previously."

"While the course is good, I recommend having a strong Python background before starting, otherwise it might be too fast."

"If you're not comfortable with basic ML concepts or Docker, you might struggle to keep up with the material presented."

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 MLOps Tools: MLflow and Hugging Face with these activities:

Organize and review class notes

Show steps

Reinforce your understanding of course concepts by organizing and reviewing your class notes effectively.

Show steps

Gather and arrange your class notes in a systematic manner.
Highlight important concepts and keywords.
Summarize each lecture or topic in your own words.
Create flashcards or mind maps to aid memorization.
Review your organized notes regularly to enhance retention.

Complete the Hugging Face Transformers Tutorial

Show steps

Gain hands-on experience with Hugging Face Transformers by following a guided tutorial, enhancing your practical understanding.

Browse courses on Hugging Face Transformers

Show steps

Visit the Hugging Face Transformers tutorial page.
Choose a tutorial that aligns with your interests and skill level.
Follow the tutorial step-by-step, completing all exercises and experiments.
Troubleshoot any errors and seek assistance from the Hugging Face community forums if needed.

Attend an MLOps Meetup

Show steps

Connect with professionals in the field of MLOps, exchange knowledge, and stay up-to-date with industry trends.

Show steps

Find an MLOps Meetup in your area or online.
Register for the event and attend.
Introduce yourself to others and participate in discussions.
Exchange business cards and connect with attendees on LinkedIn.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Contribute to MLflow examples

Show steps

Contribute to the MLflow examples repository to deepen your understanding of MLOps concepts and best practices.

Show steps

Identify an area where you can contribute, such as adding examples for a specific framework or use case.
Fork the MLflow examples repository and create a new branch for your contribution.
Implement your contribution and write clear and concise documentation.
Submit a pull request with your changes.
Collaborate with the MLflow team to review and merge your contribution.

Solve MLOps practice problems on LeetCode

Show steps

Test your understanding of MLOps concepts by solving practice problems on LeetCode, solidifying your knowledge and problem-solving skills.

Browse courses on MLOps

Show steps

Create a LeetCode account or log in.
Filter LeetCode problems using the 'MLOps' tag or search for specific MLOps-related problems.
Attempt to solve the problems on your own.
Review solutions and discuss your approaches with the LeetCode community.

Develop a cheat sheet of MLOps commands and concepts

Show steps

Create a concise and accessible cheat sheet that summarizes key MLOps commands and concepts, enhancing your quick reference and understanding.

Show steps

Identify the essential MLOps commands and concepts.
Organize the information into a logical and easy-to-navigate format.
Use clear and concise language to explain the commands and concepts.
Include examples and illustrations to enhance comprehension.
Review and refine your cheat sheet based on feedback from peers or instructors.

Volunteer at a non-profit that uses ML for social impact

Show steps

Apply your MLOps skills to make a positive impact on society by volunteering at a non-profit organization.

Browse courses on MLOps

Show steps

Research non-profit organizations that use ML for social impact.
Identify volunteer opportunities and contact the organization.
Contribute your MLOps expertise to ongoing projects or initiatives.
Network with professionals and learn about the applications of MLOps in the non-profit sector.

Career center

Learners who complete MLOps Tools: MLflow and Hugging Face will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models to solve business problems. The course on MLOps Tools: MLflow and Hugging Face provides a strong foundation for Machine Learning Engineers by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to solve business problems. They collect, clean, and analyze data to build machine learning models. The course on MLOps Tools: MLflow and Hugging Face can help Data Scientists build better machine learning models by teaching them how to use these platforms to manage and deploy models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Data Scientists.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and implement software systems. The course on MLOps Tools: MLflow and Hugging Face can help Software Engineers build better software systems by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Software Engineers.

See salaries and explore the career path for Software Engineer

DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. They ensure that software is deployed and maintained efficiently. The course on MLOps Tools: MLflow and Hugging Face can help DevOps Engineers build better software systems by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for DevOps Engineers.

See salaries and explore the career path for DevOps Engineer

Cloud Engineer

Cloud Engineers design, develop, and implement cloud-based solutions. The course on MLOps Tools: MLflow and Hugging Face can help Cloud Engineers build better cloud-based solutions by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Cloud Engineers.

See salaries and explore the career path for Cloud Engineer

Product Manager

Product Managers work to define and develop new products. They work with engineers, designers, and marketers to bring new products to market. The course on MLOps Tools: MLflow and Hugging Face can help Product Managers build better products by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Product Managers.

See salaries and explore the career path for Product Manager

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. The course on MLOps Tools: MLflow and Hugging Face can help Data Analysts build better data analysis tools by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts work with businesses to identify and solve problems. They use data and analysis to help businesses make better decisions. The course on MLOps Tools: MLflow and Hugging Face can help Business Analysts build better solutions by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Business Analysts.

See salaries and explore the career path for Business Analyst

Consultant

Consultants provide advice and guidance to businesses. They help businesses solve problems and improve their performance. The course on MLOps Tools: MLflow and Hugging Face can help Consultants build better solutions by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Consultants.

See salaries and explore the career path for Consultant

Educator

Educators teach and train students. They help students learn new skills and knowledge. The course on MLOps Tools: MLflow and Hugging Face can help Educators build better teaching materials by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Educators.

See salaries and explore the career path for Educator

Researcher

Researchers conduct research to advance knowledge in a particular field. The course on MLOps Tools: MLflow and Hugging Face can help Researchers build better research tools by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Researchers.

See salaries and explore the career path for Researcher

Writer

Writers create content for a variety of purposes, such as informing, entertaining, and persuading. The course on MLOps Tools: MLflow and Hugging Face can help Writers build better content by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Writers.

See salaries and explore the career path for Writer

Marketer

Marketers create and execute marketing campaigns to promote products and services. The course on MLOps Tools: MLflow and Hugging Face can help Marketers build better marketing campaigns by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Marketers.

See salaries and explore the career path for Marketer

Salesperson

Salespeople sell products and services to customers. The course on MLOps Tools: MLflow and Hugging Face can help Salespeople build better sales presentations by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Salespeople.

See salaries and explore the career path for Salesperson

Customer Service Representative

Customer Service Representatives provide support to customers. The course on MLOps Tools: MLflow and Hugging Face can help Customer Service Representatives build better support tools by teaching them how to use these platforms to manage and deploy machine learning models. The course covers topics such as model registration, tracking, and deployment, which are essential skills for Customer Service Representatives.

See salaries and explore the career path for Customer Service Representative

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in MLOps Tools: MLflow and Hugging Face.

Cover image

Cover image

Cross-Lingual Word Embeddings

Save

Provides a comprehensive overview of deep learning for natural language processing. It is particularly useful for learners who want to gain a deep understanding of deep learning and how to use it for a variety of NLP tasks.

Cross-Lingual Word Embeddings (Synthesis Lectures...

Cover image

Cover image

Statistical Learning with Sparsity

Save

Provides a comprehensive overview of statistical learning with sparsity. It is particularly useful for learners who want to gain a deep understanding of the theory and practice of sparse learning.

Statistical Learning with Sparsity: The Lasso and...

Statistical Learning with Sparsity: The Lasso and...

Cover image

Cover image

Introduction to Applied Linear Algebra

Save

Provides a comprehensive overview of convex optimization. It is particularly useful for learners who want to gain a deep understanding of the theory and practice of convex optimization.

Introduction to Applied Linear Algebra: Vectors,...

Introduction to Applied Linear Algebra: Vectors,...

Cover image

Cover image

An Introduction to Universal Artificial Intelligence

Save

Provides a comprehensive overview of information theory, inference, and learning algorithms. It is particularly useful for learners who want to gain a deep understanding of the theory and practice of information theory.

An Introduction to Universal Artificial...

An Introduction to Universal Artificial...

Cover image

Cover image

Reinforcement Learning, second edition

Save

Provides a comprehensive overview of reinforcement learning. It is particularly useful for learners who want to gain a deep understanding of the theory and practice of reinforcement learning.

Reinforcement Learning, second edition: An...

Reinforcement Learning, second edition: An...

Cover image

Cover image

Save

Provides a comprehensive overview of generative adversarial networks. It is particularly useful for learners who want to gain a deep understanding of the theory and practice of generative adversarial networks.

Deep Learning (Adaptive Computation and Machine...

Deep Learning (Adaptive Computation and Machine...

Cover image

Cover image

Natural Language Processing with Python

Save

Provides a comprehensive overview of natural language processing with Python. It is particularly useful for learners who want to gain a hands-on experience with NLP techniques using Python.

Natural Language Processing with Python: Analyzing...

Natural Language Processing with Python: Analyzing...

Cover image

Cover image

Machine Learning with R

Save

Provides a comprehensive overview of machine learning with R. It is particularly useful for learners who want to gain a hands-on experience with ML techniques using R.

Machine Learning with R - Fourth Edition: Learn...

Machine Learning with R - Second Edition: Expert...

Machine Learning with R - Third Edition: Expert...

(Italiano) Machine Learning con R: Conoscere le tecniche per...

R: Unleash Machine Learning Techniques

Machine Learning with R: Learn How to Use R to...

(中文) Machine learning and R language (3rd edition of the...

Cover image

Cover image

Deep Learning with Python, Second Edition

Save

Provides a comprehensive overview of deep learning with Python. It is particularly useful for learners who want to gain a hands-on experience with deep learning techniques using Python.

Deep Learning with Python, Second Edition

Deep Learning with Python, Second Edition

Share

Help others find this course page by sharing it with your friends and followers:

Copy Link

Similar courses

Similar courses are unavailable at this time. Please try again later.

Rating

3.7

Effort

25 hours to complete

Level

Advanced

Via

Coursera

Institution

Duke University

Instructors

Noah Gift

Alfredo Deza

Language

English

Transcripts

Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা پښتو آذربایجان دیلی

This course belongs to a collection called MLOps | Machine Learning Operations. It's comprised of four courses:

MLOps Tools: MLflow and Hugging Face (viewing)
Python Essentials for MLOps
DevOps, DataOps, MLOps
MLOps Platforms: Amazon SageMaker and Azure ML

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores foundational concepts of MLOps and machine learning through the prism of two popular, industry standard open source platforms: MLflow and Hugging Face

Taught by Noah Gift and Alfredo Deza, who are recognized for their work in MLOps and machine learning

Builds strong foundational skills in MLOps and machine learning through the concrete example of two open source tools

Develops professional skills and deep expertise in MLOps and machine learning, including fine-tuning, deploying, and containerizing models

Takes a creative approach to teaching MLOps and machine learning by using the lens of two popular, industry standard open source tools

Requires students to come in with some background knowledge in machine learning and programming concepts

Share this

Share to help others discover this course.

Link

Begin learning today

Enroll now to gain full access to MLOps Tools.

Enroll now Enroll in this course

Save for later

Add this course to your list. Find it anytime.

Save

Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser