Working with MLflow and Hugging Face in MLOps
This course is part of Machine Learning Operations.
In this comprehensive course, you'll master two essential MLOps platforms: MLflow and Hugging Face. The course covers streamlined machine learning lifecycle management, project and model management, tracking systems, and model deployment. You'll learn to create new MLflow projects, utilize Hugging Face models and datasets, and deploy solutions to the cloud. The curriculum includes hands-on experience with containerization, CI/CD automation, and real-world applications. Perfect for both aspiring MLOps professionals and experienced practitioners seeking to enhance their skills in machine learning operations.
Instructors:
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What you'll learn
Create and manage MLflow projects for model registration
Utilize Hugging Face models and datasets effectively
Deploy containerized machine learning solutions
Implement CI/CD pipelines for model deployment
Integrate with cloud platforms like Azure
Perform model fine-tuning and optimization
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Ungraded Labs, Discussion Prompts
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
The course provides a comprehensive introduction to MLOps tools, focusing on MLflow and Hugging Face. Students learn to manage the complete machine learning lifecycle, from project creation to model deployment. The curriculum covers essential concepts including project management, model tracking, containerization, and cloud deployment. Practical aspects include working with APIs, automation with GitHub Actions, and integration with cloud platforms. The course also addresses important considerations like fine-tuning models and ethical sourcing of datasets.
Introduction to MLflow
Module 1
Introduction to Hugging Face
Module 2
Deploying Hugging Face
Module 3
Applied Hugging Face
Module 4
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Machine Learning Operations
Instructor
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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