This course is part of the MLOps | Machine Learning Operations Specialization

Noah Gift +1 more instructor

Offered By

MLOps | Machine Learning Operations SpecializationDuke University

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

MLOps | Machine Learning Operations Specialization

Advanced Level

Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.

Approx. 12 hours to complete

English

What you will 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 will gain

Information Engineering
hugging face
Modeling
Machine Learning Software
Cloud Computing

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

MLOps | Machine Learning Operations Specialization

Advanced Level

Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.

Approx. 12 hours to complete

English

Instructors

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

44,599 Learners

12 Courses

Alfredo Deza

Adjunct Assistant Professor in the Pratt School of Engineering

22,545 Learners

8 Courses

Offered by

Duke University

Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Introduction to MLflow

3 hours to complete

13 videos (Total 82 min), 2 readings, 1 quiz

13 videos

Meet your Course Instructor: Alfredo Deza3mOverview of MLflow4mInstalling and Using MLflow5mIntroduction to the Tracking UI8mParameters, Version, Artifacts and Metrics10mWorking with MLflow Projects4mCreate an MLflow Project7mRun Project from Remote Git Repositories3mConnecting MLflow to Databricks5mComponents of an MLflow Package6mUsing a Registry with an MLflow Model5mReferencing Artifacts with the API8mSaving and Serving MLflow Models8m

2 readings

Meet your Supporting Instructor: Noah Gift10mCourse Structure and Discussion Etiquette10m

1 practice exercise

MLflow30m

Week2

Week 2

3 hours to complete

Introduction to Hugging Face

3 hours to complete

14 videos (Total 98 min)

14 videos

What is Hugging Face?5mOverview of the Hugging Face Hub5mIntroduction to the Hugging Face Hub5mUsing Hugging Face Repositories7mUsing Hugging Face Spaces12mIntroduction to Applied Hugging Face1mUsing GPU Enabled Codespaces8mUsing the Hugging Face CLI2mUsing the Model Hub7mDownloading Models7mWorking with Models9mAdding Datasets6mUsing Datasets10mWorking with Datasets6m

1 practice exercise

Hugging Face Fundamentals30m

Week3

Week 3

3 hours to complete

Deploying Hugging Face

3 hours to complete

13 videos (Total 76 min)

13 videos

Hugging Face and FastAPI4mContainerizing Hugging Face3mRunning FastAPI with Hugging Face7mCI/CD Packaging with GitHub Actions9mHugging Face and Azure ML Studio4mRegistering a Hugging Face Dataset on Azure7mRegistering a Hugging Face Model on Azure5mInspecting a Hugging Face Dataset on Azure2mAzure ML Python SDK5mUsing GitHub Actions for Model Deployments5mUsing Azure Container Registry3mAutomating Packaging with Azure Container Registry7mAutomating Packaging with Docker Hub7m

1 practice exercise

Deploying Hugging Face30m

Week4

Week 4

4 hours to complete

Applied Hugging Face

4 hours to complete

11 videos (Total 65 min)

11 videos

Create an Azure Container Application5mConfigure an Azure Container Application5mDeploy Hugging Face to Azure12mTroubleshooting Container Deployment4mIntroduction to Fine-Tuning Theory2mPerforming Fine-Tuning8mIntroduction to ONNX and Hugging Face8mExporting Hugging Face Models to ONNX4mIntroduction to Hugging Face Spaces4mHugging Face Spaces Walkthrough6mDeploying Hugging Face Spaces3m

1 practice exercise

Applied Hugging Face30m

About the MLOps | Machine Learning Operations Specialization

This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps

Through this series, you will begin to learn skills for various career paths: 1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making. 2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems. 3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner. 4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

Frequently Asked Questions

When will I have access to the lectures and assignments?
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.
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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.

More questions? Visit the Learner Help Center.

MLOps Tools: MLflow and Hugging Face

About this Course

What you will learn

Skills you will gain