Duke University - MLOps Tools: MLflow and Hugging Face
- Offered byCoursera
MLOps Tools: MLflow and Hugging Face at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
MLOps Tools: MLflow and Hugging Face at Coursera Highlights
- 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.
- 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 Subtitles: English
MLOps Tools: MLflow and Hugging Face at Coursera Course details
- 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.
MLOps Tools: MLflow and Hugging Face at Coursera Curriculum
Introduction to MLflow
Meet your Course Instructor: Alfredo Deza
Overview of MLflow
Installing and Using MLflow
Introduction to the Tracking UI
Parameters, Version, Artifacts and Metrics
Working with MLflow Projects
Create an MLflow Project
Run Project from Remote Git Repositories
Connecting MLflow to Databricks
Components of an MLflow Package
Using a Registry with an MLflow Model
Referencing Artifacts with the API
Saving and Serving MLflow Models
Meet your Supporting Instructor: Noah Gift
Course Structure and Discussion Etiquette
MLflow
Introduction to Hugging Face
What is Hugging Face?
Overview of the Hugging Face Hub
Introduction to the Hugging Face Hub
Using Hugging Face Repositories
Using Hugging Face Spaces
Introduction to Applied Hugging Face
Using GPU Enabled Codespaces
Using the Hugging Face CLI
Using the Model Hub
Downloading Models
Working with Models
Adding Datasets
Using Datasets
Working with Datasets
Hugging Face Fundamentals
Deploying Hugging Face
Hugging Face and FastAPI
Containerizing Hugging Face
Running FastAPI with Hugging Face
CI/CD Packaging with GitHub Actions
Hugging Face and Azure ML Studio
Registering a Hugging Face Dataset on Azure
Registering a Hugging Face Model on Azure
Inspecting a Hugging Face Dataset on Azure
Azure ML Python SDK
Using GitHub Actions for Model Deployments
Using Azure Container Registry
Automating Packaging with Azure Container Registry
Automating Packaging with Docker Hub
Deploying Hugging Face
Applied Hugging Face
Create an Azure Container Application
Configure an Azure Container Application
Deploy Hugging Face to Azure
Troubleshooting Container Deployment
Introduction to Fine-Tuning Theory
Performing Fine-Tuning
Introduction to ONNX and Hugging Face
Exporting Hugging Face Models to ONNX
Introduction to Hugging Face Spaces
Hugging Face Spaces Walkthrough
Deploying Hugging Face Spaces
Applied Hugging Face
MLOps Tools: MLflow and Hugging Face at Coursera Admission Process
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