课程信息

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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.

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

高级

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

完成时间大约为13 小时

英语（English）

您将学到的内容有

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.

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

高级

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

完成时间大约为13 小时

英语（English）

授课教师

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

32,730 个学生

10 门课程

Alfredo Deza

Senior Content Engineer

Microsoft

13,745 个学生

6 门课程

授课大纲 - 您将从这门课程中学到什么

周

1

周 1

完成时间为 3 小时

Introduction to MLflow

完成时间为 3 小时

13 个视频（总计 82 分钟）, 2 个阅读材料, 1 个测验

13 个视频

Meet your Course Instructor: Alfredo Deza3分钟

Overview of MLflow4分钟

Installing and Using MLflow5分钟

Introduction to the Tracking UI8分钟

Parameters, Version, Artifacts and Metrics10分钟

Working with MLflow Projects4分钟

Create an MLflow Project7分钟

Run Project from Remote Git Repositories3分钟

Connecting MLflow to Databricks5分钟

Components of an MLflow Package6分钟

Using a Registry with an MLflow Model5分钟

Referencing Artifacts with the API8分钟

Saving and Serving MLflow Models8分钟

2 个阅读材料

Meet your Supporting Instructor: Noah Gift10分钟

Course Structure and Discussion Etiquette10分钟

1 个练习

MLflow30分钟

周

2

周 2

完成时间为 3 小时

Introduction to Hugging Face

完成时间为 3 小时

14 个视频（总计 98 分钟）

14 个视频

What is Hugging Face?5分钟

Overview of the Hugging Face Hub5分钟

Introduction to the Hugging Face Hub5分钟

Using Hugging Face Repositories7分钟

Using Hugging Face Spaces12分钟

Introduction to Applied Hugging Face1分钟

Using GPU Enabled Codespaces8分钟

Using the Hugging Face CLI2分钟

Using the Model Hub7分钟

Downloading Models7分钟

Working with Models9分钟

Adding Datasets6分钟

Using Datasets10分钟

Working with Datasets6分钟

1 个练习

Hugging Face Fundamentals30分钟

周

3

周 3

完成时间为 3 小时

Deploying Hugging Face

完成时间为 3 小时

13 个视频（总计 76 分钟）

13 个视频

Hugging Face and FastAPI4分钟

Containerizing Hugging Face3分钟

Running FastAPI with Hugging Face7分钟

CI/CD Packaging with GitHub Actions9分钟

Hugging Face and Azure ML Studio4分钟

Registering a Hugging Face Dataset on Azure7分钟

Registering a Hugging Face Model on Azure5分钟

Inspecting a Hugging Face Dataset on Azure2分钟

Azure ML Python SDK5分钟

Using GitHub Actions for Model Deployments5分钟

Using Azure Container Registry3分钟

Automating Packaging with Azure Container Registry7分钟

Automating Packaging with Docker Hub7分钟

1 个练习

Deploying Hugging Face30分钟

周

4

周 4

完成时间为 4 小时

Applied Hugging Face

完成时间为 4 小时

11 个视频（总计 65 分钟）

11 个视频

Create an Azure Container Application5分钟

Configure an Azure Container Application5分钟

Deploy Hugging Face to Azure12分钟

Troubleshooting Container Deployment4分钟

Introduction to Fine-Tuning Theory2分钟

Performing Fine-Tuning8分钟

Introduction to ONNX and Hugging Face8分钟

Exporting Hugging Face Models to ONNX4分钟

Introduction to Hugging Face Spaces4分钟

Hugging Face Spaces Walkthrough6分钟

Deploying Hugging Face Spaces3分钟

1 个练习

Applied Hugging Face30分钟

常见问题

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.

还有其他问题吗？请访问学生帮助中心。

Open Source Platforms for MLOps