Does your course require any paid software for course completion?

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.

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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.

Is financial aid available?

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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

12,649 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

67 reviews

Advanced level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

67 reviews

Advanced level

Recommended experience

3 weeks to complete

at 10 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

Module details

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 videos13 readings3 assignments1 discussion prompt1 ungraded lab

13 videosTotal 82 minutes

Meet your Course Instructor: Alfredo Deza3 minutes
Overview of MLflow4 minutes
Installing and Using MLflow6 minutes
Introduction to the Tracking UI9 minutes
Parameters, Version, Artifacts and Metrics10 minutes
Working with MLflow Projects5 minutes
Create an MLflow Project8 minutes
Run Project from Remote Git Repositories4 minutes
Connecting MLflow to Databricks5 minutes
Components of an MLflow Package6 minutes
Using a Registry with an MLflow Model5 minutes
Referencing Artifacts with the API8 minutes
Saving and Serving MLflow Models8 minutes

13 readingsTotal 130 minutes

Meet your Supporting Instructor: Noah Gift10 minutes
Course Structure and Discussion Etiquette10 minutes
Getting Started and Best Practices10 minutes
Report a problem with the course10 minutes
Key Terms10 minutes
What is MLFlow?10 minutes
Lesson Reflection10 minutes
Key Terms10 minutes
MLflow Projects 10 minutes
Lesson Reflection10 minutes
Key Terms10 minutes
MLflow Models10 minutes
Lesson Reflection10 minutes

3 assignmentsTotal 90 minutes

MLflow30 minutes
Introduction to MLFlow30 minutes
MLflow Projects30 minutes

1 discussion promptTotal 10 minutes

Meet and Greet (optional)10 minutes

1 ungraded labTotal 60 minutes

MLflow Projects60 minutes

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

14 videosTotal 98 minutes

What is Hugging Face?6 minutes
Overview of the Hugging Face Hub5 minutes
Introduction to the Hugging Face Hub5 minutes
Using Hugging Face Repositories8 minutes
Using Hugging Face Spaces13 minutes
Introduction to Applied Hugging Face2 minutes
Using GPU Enabled Codespaces8 minutes
Using the Hugging Face CLI2 minutes
Using the Model Hub7 minutes
Downloading Models8 minutes
Working with Models10 minutes
Adding Datasets7 minutes
Using Datasets11 minutes
Working with Datasets7 minutes

9 readingsTotal 90 minutes

Key Terms10 minutes
Hugging Face Hub10 minutes
Lesson Reflection10 minutes
Key Terms10 minutes
Hugging Face CLI10 minutes
Lesson Reflection10 minutes
Key Terms10 minutes
Datasets10 minutes
Lesson Reflection10 minutes

1 assignmentTotal 30 minutes

Hugging Face Fundamentals30 minutes

1 ungraded labTotal 60 minutes

Introduction to Hugging Face60 minutes

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

13 videosTotal 75 minutes

Hugging Face and FastAPI4 minutes
Containerizing Hugging Face4 minutes
Running FastAPI with Hugging Face8 minutes
CI/CD Packaging with GitHub Actions10 minutes
Hugging Face and Azure ML Studio5 minutes
Registering a Hugging Face Dataset on Azure8 minutes
Registering a Hugging Face Model on Azure6 minutes
Inspecting a Hugging Face Dataset on Azure3 minutes
Azure ML Python SDK6 minutes
Using GitHub Actions for Model Deployments6 minutes
Using Azure Container Registry4 minutes
Automating Packaging with Azure Container Registry7 minutes
Automating Packaging with Docker Hub6 minutes

9 readingsTotal 90 minutes

Key Terms10 minutes
FastAPI10 minutes
Lesson Reflection10 minutes
Key Terms10 minutes
Azure ML Python SDK10 minutes
Lesson Reflection10 minutes
Key Terms10 minutes
Docker Overview10 minutes
Lesson Reflection10 minutes

3 assignmentsTotal 90 minutes

Deploying Hugging Face30 minutes
Quiz-Packaging Hugging Face30 minutes
Hugging Face and Azure30 minutes

1 ungraded labTotal 60 minutes

Packaging Hugging Face60 minutes

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.