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MLOps Tools: MLflow and Hugging Face

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MLOps Tools: MLflow and Hugging Face

Name: MLOps Tools: MLflow and Hugging Face
Rating: 3.6984126984126986 (63 reviews)

This course is part of MLOps | Machine Learning Operations Specialization

Instructors: Noah Gift

12,073 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

63 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.

63 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

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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.

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.

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 assignments2 discussion prompts1 ungraded lab

13 videos Total 82 minutes

Meet your Course Instructor: Alfredo Deza 3 minutes
Overview of MLflow 4 minutes
Installing and Using MLflow 6 minutes
Introduction to the Tracking UI 9 minutes
Parameters, Version, Artifacts and Metrics 10 minutes
Working with MLflow Projects 5 minutes
Create an MLflow Project 8 minutes
Run Project from Remote Git Repositories 4 minutes
Connecting MLflow to Databricks 5 minutes
Components of an MLflow Package 6 minutes
Using a Registry with an MLflow Model 5 minutes
Referencing Artifacts with the API 8 minutes
Saving and Serving MLflow Models 8 minutes

13 readings Total 130 minutes

Meet your Supporting Instructor: Noah Gift 10 minutes
Course Structure and Discussion Etiquette 10 minutes
Getting Started and Best Practices 10 minutes
Report a problem with the course 10 minutes
Key Terms 10 minutes
What is MLFlow? 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
MLflow Projects 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
MLflow Models 10 minutes
Lesson Reflection 10 minutes

3 assignments Total 90 minutes

MLflow 30 minutes
Introduction to MLFlow 30 minutes
MLflow Projects 30 minutes

2 discussion prompts Total 20 minutes

Meet and Greet (optional) 10 minutes
Let Us Know if Something’s Not Working 10 minutes

1 ungraded lab Total 60 minutes

MLflow Projects 60 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 videos Total 98 minutes

What is Hugging Face? 6 minutes
Overview of the Hugging Face Hub 5 minutes
Introduction to the Hugging Face Hub 5 minutes
Using Hugging Face Repositories 8 minutes
Using Hugging Face Spaces 13 minutes
Introduction to Applied Hugging Face 2 minutes
Using GPU Enabled Codespaces 8 minutes
Using the Hugging Face CLI 2 minutes
Using the Model Hub 7 minutes
Downloading Models 8 minutes
Working with Models 10 minutes
Adding Datasets 7 minutes
Using Datasets 11 minutes
Working with Datasets 7 minutes

9 readings Total 90 minutes

Key Terms 10 minutes
Hugging Face Hub 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
Hugging Face CLI 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
Datasets 10 minutes
Lesson Reflection 10 minutes

1 assignment Total 30 minutes

Hugging Face Fundamentals 30 minutes

1 ungraded lab Total 60 minutes

Introduction to Hugging Face 60 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 videos Total 75 minutes

Hugging Face and FastAPI 4 minutes
Containerizing Hugging Face 4 minutes
Running FastAPI with Hugging Face 8 minutes
CI/CD Packaging with GitHub Actions 10 minutes
Hugging Face and Azure ML Studio 5 minutes
Registering a Hugging Face Dataset on Azure 8 minutes
Registering a Hugging Face Model on Azure 6 minutes
Inspecting a Hugging Face Dataset on Azure 3 minutes
Azure ML Python SDK 6 minutes
Using GitHub Actions for Model Deployments 6 minutes
Using Azure Container Registry 4 minutes
Automating Packaging with Azure Container Registry 7 minutes
Automating Packaging with Docker Hub 6 minutes

9 readings Total 90 minutes

Key Terms 10 minutes
FastAPI 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
Azure ML Python SDK 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
Docker Overview 10 minutes
Lesson Reflection 10 minutes

3 assignments Total 90 minutes

Deploying Hugging Face 30 minutes
Quiz-Packaging Hugging Face 30 minutes
Hugging Face and Azure 30 minutes

1 ungraded lab Total 60 minutes

Packaging Hugging Face 60 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.

What's included

17 videos11 readings3 assignments5 ungraded labs

17 videos Total 90 minutes

Create an Azure Container Application 5 minutes
Configure an Azure Container Application 5 minutes
Deploy Hugging Face to Azure 12 minutes
Troubleshooting Container Deployment 4 minutes
Introduction to Fine-Tuning Theory 3 minutes
Performing Fine-Tuning 8 minutes
Introduction to ONNX and Hugging Face 9 minutes
Exporting Hugging Face Models to ONNX 4 minutes
Introduction to Hugging Face Spaces 5 minutes
Hugging Face Spaces Walkthrough 6 minutes
Deploying Hugging Face Spaces 3 minutes
Profit Sharing Concepts 6 minutes
Tragedy of the GenAI commons 4 minutes
Game Theory of GenAI 5 minutes
Perfect Competition 3 minutes
Negative Externalities 3 minutes
Regulatory Entrepreneurship 4 minutes

11 readings Total 110 minutes

Key Terms 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
Lesson Reflection 10 minutes
Key Terms 10 minutes
Regulatory Entrepreneurship 10 minutes
Ethical Sourcing of Datasets 10 minutes
Glaze 10 minutes
Lesson Reflection 10 minutes
Next Steps 10 minutes
Share your learning experience 10 minutes

3 assignments Total 90 minutes

Applied Hugging Face 30 minutes
Quiz-Hugging Face with Azure Containers 30 minutes
Quiz: Fine-Tuning and ONNX Exporting 30 minutes

5 ungraded labs Total 300 minutes

Hugging Face and ONNX 60 minutes
Deploying Hugging Face 60 minutes
Final Jupyter TensorFlow Sandbox 60 minutes
VSCode Final Sandbox 60 minutes
Linux Desktop Final Desktop 60 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Instructor ratings

(14 ratings)

Noah Gift

Duke University

40 Courses 262,835 learners

Offered by

Duke University

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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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.