課程信息

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This course covers two of the most popular open source platforms for MLOps: 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. Through a series of hands-on exercises, learners will gain practical experience working with these open source platforms. 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.

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

高級

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

完成時間大約為13 小時

英語（English）

您將學到的內容有

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.

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

高級

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

完成時間大約為13 小時

英語（English）

授課教師

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

32,888 個學生

10 門課程

Alfredo Deza

Senior Content Engineer

Microsoft

13,865 個學生

6 門課程

授課大綱 - 您將從這門課程中學到什麼

週

1

週 1

完成時間為 3 小時

Introduction to MLflow

完成時間為 3 小時

13 個視頻（總計 82 分鐘）, 2 個閱讀材料, 1 個測驗

13 個視頻

Meet your Course Instructor: Alfredo Deza3分鐘

Overview of MLflow4分鐘

Installing and Using MLflow5分鐘

Introduction to the Tracking UI8分鐘

Parameters, Version, Artifacts and Metrics10分鐘

Working with MLflow Projects4分鐘

Create an MLflow Project7分鐘

Run Project from Remote Git Repositories3分鐘

Connecting MLflow to Databricks5分鐘

Components of an MLflow Package6分鐘

Using a Registry with an MLflow Model5分鐘

Referencing Artifacts with the API8分鐘

Saving and Serving MLflow Models8分鐘

2 個閱讀材料

Meet your Supporting Instructor: Noah Gift10分鐘

Course Structure and Discussion Etiquette10分鐘

1 個練習

MLflow30分鐘

週

2

週 2

完成時間為 3 小時

Introduction to Hugging Face

完成時間為 3 小時

14 個視頻（總計 98 分鐘）

14 個視頻

What is Hugging Face?5分鐘

Overview of the Hugging Face Hub5分鐘

Introduction to the Hugging Face Hub5分鐘

Using Hugging Face Repositories7分鐘

Using Hugging Face Spaces12分鐘

Introduction to Applied Hugging Face1分鐘

Using GPU Enabled Codespaces8分鐘

Using the Hugging Face CLI2分鐘

Using the Model Hub7分鐘

Downloading Models7分鐘

Working with Models9分鐘

Adding Datasets6分鐘

Using Datasets10分鐘

Working with Datasets6分鐘

1 個練習

Hugging Face Fundamentals30分鐘

週

3

週 3

完成時間為 3 小時

Deploying Hugging Face

完成時間為 3 小時

13 個視頻（總計 76 分鐘）

13 個視頻

Hugging Face and FastAPI4分鐘

Containerizing Hugging Face3分鐘

Running FastAPI with Hugging Face7分鐘

CI/CD Packaging with GitHub Actions9分鐘

Hugging Face and Azure ML Studio4分鐘

Registering a Hugging Face Dataset on Azure7分鐘

Registering a Hugging Face Model on Azure5分鐘

Inspecting a Hugging Face Dataset on Azure2分鐘

Azure ML Python SDK5分鐘

Using GitHub Actions for Model Deployments5分鐘

Using Azure Container Registry3分鐘

Automating Packaging with Azure Container Registry7分鐘

Automating Packaging with Docker Hub7分鐘

1 個練習

Deploying Hugging Face30分鐘

週

4

週 4

完成時間為 4 小時

Applied Hugging Face

完成時間為 4 小時

11 個視頻（總計 65 分鐘）

11 個視頻

Create an Azure Container Application5分鐘

Configure an Azure Container Application5分鐘

Deploy Hugging Face to Azure12分鐘

Troubleshooting Container Deployment4分鐘

Introduction to Fine-Tuning Theory2分鐘

Performing Fine-Tuning8分鐘

Introduction to ONNX and Hugging Face8分鐘

Exporting Hugging Face Models to ONNX4分鐘

Introduction to Hugging Face Spaces4分鐘

Hugging Face Spaces Walkthrough6分鐘

Deploying Hugging Face Spaces3分鐘

1 個練習

Applied Hugging Face30分鐘

常見問題

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 purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, 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.

還有其他問題嗎？請訪問學生幫助中心。

Open Source Platforms for MLOps