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.
MLOps Tools: MLflow and Hugging Face
MLOps Tools: MLflow and Hugging Face
This course is part of MLOps | Machine Learning Operations Specialization
Instructors: Noah Gift
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3.8
(39 reviews)
Advanced level
Recommended experience
25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Create new MLflow projects to create and register models.
Use Hugging Face models and datasets to build your own APIs.
Package and deploy Hugging Face to the Cloud using automation.
Skills you'll gain
Details to know
Shareable certificate
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Assessments
10 assignments
Taught in English
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This course is part of the MLOps | Machine Learning Operations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module, 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.
What's included
13 videos12 readings3 assignments2 discussion prompts1 ungraded lab
In this module, 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.
What's included
14 videos9 readings1 assignment1 ungraded lab
In this module, 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.
What's included
13 videos9 readings3 assignments1 ungraded lab
In this module, 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.
What's included
17 videos10 readings3 assignments5 ungraded labs
Offered by
Recommended if you're interested in Machine Learning
Google Cloud
Google Cloud
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3.8
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Reviewed on Aug 21, 2024
Great learning resources that will be useful long after completing the course, concise presentations, and clear explanations of all topics
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Frequently asked questions
No, exercises and labs are built directly into the course using integrated Coursera Labs (VS Code + Jupyter Notebooks). A few exercises guide learners in deploying models to the Cloud. In those cases, instructions are provided to learners for creating and accessing a free Azure account.
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.
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.