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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.

유연한 마감일

일정에 따라 마감일을 재설정합니다.

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

고급 단계

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

완료하는 데 약 13시간 필요

영어

배울 내용

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.

유연한 마감일

일정에 따라 마감일을 재설정합니다.

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

고급 단계

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

완료하는 데 약 13시간 필요

영어

강사

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

33,154명의 학습자

10개의 강좌

Alfredo Deza

Senior Content Engineer

Microsoft

14,061명의 학습자

6개의 강좌

강의 계획표 - 이 강좌에서 배울 내용

주

1

주 1

완료하는 데 3시간 필요

Introduction to MLflow

완료하는 데 3시간 필요

13개 동영상 (총 82분), 2 개의 읽기 자료, 1 개의 테스트

13개의 동영상

Meet your Course Instructor: Alfredo Deza3m

Overview of MLflow4m

Installing and Using MLflow5m

Introduction to the Tracking UI8m

Parameters, Version, Artifacts and Metrics10m

Working with MLflow Projects4m

Create an MLflow Project7m

Run Project from Remote Git Repositories3m

Connecting MLflow to Databricks5m

Components of an MLflow Package6m

Using a Registry with an MLflow Model5m

Referencing Artifacts with the API8m

Saving and Serving MLflow Models8m

2개의 읽기 자료

Meet your Supporting Instructor: Noah Gift10m

Course Structure and Discussion Etiquette10m

1개 연습문제

MLflow30m

주

2

주 2

완료하는 데 3시간 필요

Introduction to Hugging Face

완료하는 데 3시간 필요

14개 동영상 (총 98분)

14개의 동영상

What is Hugging Face?5m

Overview of the Hugging Face Hub5m

Introduction to the Hugging Face Hub5m

Using Hugging Face Repositories7m

Using Hugging Face Spaces12m

Introduction to Applied Hugging Face1m

Using GPU Enabled Codespaces8m

Using the Hugging Face CLI2m

Using the Model Hub7m

Downloading Models7m

Working with Models9m

Adding Datasets6m

Using Datasets10m

Working with Datasets6m

1개 연습문제

Hugging Face Fundamentals30m

주

3

주 3

완료하는 데 3시간 필요

Deploying Hugging Face

완료하는 데 3시간 필요

13개 동영상 (총 76분)

13개의 동영상

Hugging Face and FastAPI4m

Containerizing Hugging Face3m

Running FastAPI with Hugging Face7m

CI/CD Packaging with GitHub Actions9m

Hugging Face and Azure ML Studio4m

Registering a Hugging Face Dataset on Azure7m

Registering a Hugging Face Model on Azure5m

Inspecting a Hugging Face Dataset on Azure2m

Azure ML Python SDK5m

Using GitHub Actions for Model Deployments5m

Using Azure Container Registry3m

Automating Packaging with Azure Container Registry7m

Automating Packaging with Docker Hub7m

1개 연습문제

Deploying Hugging Face30m

주

4

주 4

완료하는 데 4시간 필요

Applied Hugging Face

완료하는 데 4시간 필요

11개 동영상 (총 65분)

11개의 동영상

Create an Azure Container Application5m

Configure an Azure Container Application5m

Deploy Hugging Face to Azure12m

Troubleshooting Container Deployment4m

Introduction to Fine-Tuning Theory2m

Performing Fine-Tuning8m

Introduction to ONNX and Hugging Face8m

Exporting Hugging Face Models to ONNX4m

Introduction to Hugging Face Spaces4m

Hugging Face Spaces Walkthrough6m

Deploying Hugging Face Spaces3m

1개 연습문제

Applied Hugging Face30m

자주 묻는 질문

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.

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Open Source Platforms for MLOps