このコースについて

12,282 最近の表示

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.

柔軟性のある期限

スケジュールに従って期限をリセットします。

共有できる証明書

修了時に証明書を取得

100%オンライン

自分のスケジュールですぐに学習を始めてください。

上級レベル

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

約13時間で修了

英語

学習内容

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.

柔軟性のある期限

スケジュールに従って期限をリセットします。

共有できる証明書

修了時に証明書を取得

100%オンライン

自分のスケジュールですぐに学習を始めてください。

上級レベル

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

約13時間で修了

英語

講師

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

受講者32,888 名

10 コース

Alfredo Deza

Senior Content Engineer

Microsoft

受講者13,865 名

6 コース

シラバス - 本コースの学習内容

週

1

週 1

3時間で修了

Introduction to MLflow

3時間で修了

13件のビデオ（合計82分）, 2 学習用教材, 1 個のテスト

13件のビデオ

Meet your Course Instructor: Alfredo Deza3 分

Overview of MLflow4 分

Installing and Using MLflow5 分

Introduction to the Tracking UI8 分

Parameters, Version, Artifacts and Metrics10 分

Working with MLflow Projects4 分

Create an MLflow Project7 分

Run Project from Remote Git Repositories3 分

Connecting MLflow to Databricks5 分

Components of an MLflow Package6 分

Using a Registry with an MLflow Model5 分

Referencing Artifacts with the API8 分

Saving and Serving MLflow Models8 分

2件の学習用教材

Meet your Supporting Instructor: Noah Gift10 分

Course Structure and Discussion Etiquette10 分

1の練習問題

MLflow30 分

週

2

週 2

3時間で修了

Introduction to Hugging Face

3時間で修了

14件のビデオ（合計98分）

14件のビデオ

What is Hugging Face?5 分

Overview of the Hugging Face Hub5 分

Introduction to the Hugging Face Hub5 分

Using Hugging Face Repositories7 分

Using Hugging Face Spaces12 分

Introduction to Applied Hugging Face1 分

Using GPU Enabled Codespaces8 分

Using the Hugging Face CLI2 分

Using the Model Hub7 分

Downloading Models7 分

Working with Models9 分

Adding Datasets6 分

Using Datasets10 分

Working with Datasets6 分

1の練習問題

Hugging Face Fundamentals30 分

週

3

週 3

3時間で修了

Deploying Hugging Face

3時間で修了

13件のビデオ（合計76分）

13件のビデオ

Hugging Face and FastAPI4 分

Containerizing Hugging Face3 分

Running FastAPI with Hugging Face7 分

CI/CD Packaging with GitHub Actions9 分

Hugging Face and Azure ML Studio4 分

Registering a Hugging Face Dataset on Azure7 分

Registering a Hugging Face Model on Azure5 分

Inspecting a Hugging Face Dataset on Azure2 分

Azure ML Python SDK5 分

Using GitHub Actions for Model Deployments5 分

Using Azure Container Registry3 分

Automating Packaging with Azure Container Registry7 分

Automating Packaging with Docker Hub7 分

1の練習問題

Deploying Hugging Face30 分

週

4

週 4

4時間で修了

Applied Hugging Face

4時間で修了

11件のビデオ（合計65分）

11件のビデオ

Create an Azure Container Application5 分

Configure an Azure Container Application5 分

Deploy Hugging Face to Azure12 分

Troubleshooting Container Deployment4 分

Introduction to Fine-Tuning Theory2 分

Performing Fine-Tuning8 分

Introduction to ONNX and Hugging Face8 分

Exporting Hugging Face Models to ONNX4 分

Introduction to Hugging Face Spaces4 分

Hugging Face Spaces Walkthrough6 分

Deploying Hugging Face Spaces3 分

1の練習問題

Applied Hugging Face30 分

よくある質問

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.

さらに質問がある場合は、受講者ヘルプセンターにアクセスしてください。

Open Source Platforms for MLOps