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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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/fz3y1k/mlops
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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
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
Introduction to Hugging Face
This week, you will learn the basics of the Hugging Face platform. You will use some of its features like its repositories so that you can store models and datasets. Finally, you will learn how to add and use models and datasets using Hugging Face APIs as well as the web interface.
Deploying Hugging Face
This week, you will learn how to containerize Hugging Face models and use the FastAPI framework to serve the model with an interactive HTTP API endpoint. Once you understand how to put everything together, you’ll use automation for speed and reproducibility. Finally, you’ll use Azure and Docker Hub to store the containers so that they can be used later for deployments.
Applied Hugging Face
This week, you will learn how to fine-tune Hugging Face models by using pre-existing models and then modifying (fine-tuning) them with additional data. You’ll also use Azure to deploy the container and learn how to troubleshoot it. Finally, you’ll also see how to deploy a model to Hugging Face spaces.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save MLOps Tools: MLflow and Hugging Face to your list so you can find it easily later:
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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.
Organize and review class notes
Show steps
Reinforce your understanding of course concepts by organizing and reviewing your class notes effectively.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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 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.
Data Scientist
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.
Software Engineer
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.
DevOps 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.
Cloud 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.
Product Manager
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.
Data Analyst
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.
Business 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.
Consultant
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.
Marketer
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.
Customer Service Representative
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.
Researcher
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.
Educator
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.
Writer
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.
Salesperson
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.
For more career information including salaries, visit:
OpenCourser.com/course/fz3y1k/mlops
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fz3y1k/mlops
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Effort
4 weeks of study, 3-5 hours/week
Level
Advanced
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Language
English
Transcripts
Español
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Deutsch
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中文
العربية
한국어
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فارسی
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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
Good to know
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
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