Über diesen Kurs

11.054 kürzliche Aufrufe

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.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Stufe „Fortgeschritten“

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

Ca. 13 Stunden zum Abschließen

Englisch

Was Sie lernen werden

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.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Stufe „Fortgeschritten“

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

Ca. 13 Stunden zum Abschließen

Englisch

Dozenten

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

32.122 Lernende

10 Kurse

Alfredo Deza

Senior Content Engineer

Microsoft

13.298 Lernende

6 Kurse

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche

1

Woche 1

3 Stunden zum Abschließen

Introduction to MLflow

3 Stunden zum Abschließen

13 Videos (Gesamt 82 min), 2 Lektüren, 1 Quiz

13 Videos

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

2 Lektüren

Meet your Supporting Instructor: Noah Gift10m

Course Structure and Discussion Etiquette10m

1 praktische Übung

MLflow30m

Woche

2

Woche 2

3 Stunden zum Abschließen

Introduction to Hugging Face

3 Stunden zum Abschließen

14 Videos (Gesamt 98 min)

14 Videos

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

1 praktische Übung

Hugging Face Fundamentals30m

Woche

3

Woche 3

3 Stunden zum Abschließen

Deploying Hugging Face

3 Stunden zum Abschließen

13 Videos (Gesamt 76 min)

13 Videos

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

1 praktische Übung

Deploying Hugging Face30m

Woche

4

Woche 4

4 Stunden zum Abschließen

Applied Hugging Face

4 Stunden zum Abschließen

11 Videos (Gesamt 65 min)

11 Videos

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

1 praktische Übung

Applied Hugging Face30m

Häufig gestellte Fragen

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