Об этом курсе

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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.

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Сертификат, ссылками на который можно делиться с другими людьми

Получите сертификат по завершении

100% онлайн

Начните сейчас и учитесь по собственному графику.

Продвинутый уровень

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

Прибл. 13 часов на выполнение

Английский

Чему вы научитесь

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.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Сертификат, ссылками на который можно делиться с другими людьми

Получите сертификат по завершении

100% онлайн

Начните сейчас и учитесь по собственному графику.

Продвинутый уровень

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

Прибл. 13 часов на выполнение

Английский

Преподаватели

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

32 888 учащихся

10 курсов

Alfredo Deza

Senior Content Engineer

Microsoft

13 865 учащихся

6 курсов

Программа курса: что вы изучите

Неделя

1

Неделя 1

3 ч. на завершение

Introduction to MLflow

3 ч. на завершение

13 видео ((всего 82 мин.)), 2 материалов для самостоятельного изучения, 1 тест

13 видео

Meet your Course Instructor: Alfredo Deza3мин

Overview of MLflow4мин

Installing and Using MLflow5мин

Introduction to the Tracking UI8мин

Parameters, Version, Artifacts and Metrics10мин

Working with MLflow Projects4мин

Create an MLflow Project7мин

Run Project from Remote Git Repositories3мин

Connecting MLflow to Databricks5мин

Components of an MLflow Package6мин

Using a Registry with an MLflow Model5мин

Referencing Artifacts with the API8мин

Saving and Serving MLflow Models8мин

2 материала для самостоятельного изучения

Meet your Supporting Instructor: Noah Gift10мин

Course Structure and Discussion Etiquette10мин

1 практическое упражнение

MLflow30мин

Неделя

2

Неделя 2

3 ч. на завершение

Introduction to Hugging Face

3 ч. на завершение

14 видео ((всего 98 мин.))

14 видео

What is Hugging Face?5мин

Overview of the Hugging Face Hub5мин

Introduction to the Hugging Face Hub5мин

Using Hugging Face Repositories7мин

Using Hugging Face Spaces12мин

Introduction to Applied Hugging Face1мин

Using GPU Enabled Codespaces8мин

Using the Hugging Face CLI2мин

Using the Model Hub7мин

Downloading Models7мин

Working with Models9мин

Adding Datasets6мин

Using Datasets10мин

Working with Datasets6мин

1 практическое упражнение

Hugging Face Fundamentals30мин

Неделя

3

Неделя 3

3 ч. на завершение

Deploying Hugging Face

3 ч. на завершение

13 видео ((всего 76 мин.))

13 видео

Hugging Face and FastAPI4мин

Containerizing Hugging Face3мин

Running FastAPI with Hugging Face7мин

CI/CD Packaging with GitHub Actions9мин

Hugging Face and Azure ML Studio4мин

Registering a Hugging Face Dataset on Azure7мин

Registering a Hugging Face Model on Azure5мин

Inspecting a Hugging Face Dataset on Azure2мин

Azure ML Python SDK5мин

Using GitHub Actions for Model Deployments5мин

Using Azure Container Registry3мин

Automating Packaging with Azure Container Registry7мин

Automating Packaging with Docker Hub7мин

1 практическое упражнение

Deploying Hugging Face30мин

Неделя

4

Неделя 4

4 ч. на завершение

Applied Hugging Face

4 ч. на завершение

11 видео ((всего 65 мин.))

11 видео

Create an Azure Container Application5мин

Configure an Azure Container Application5мин

Deploy Hugging Face to Azure12мин

Troubleshooting Container Deployment4мин

Introduction to Fine-Tuning Theory2мин

Performing Fine-Tuning8мин

Introduction to ONNX and Hugging Face8мин

Exporting Hugging Face Models to ONNX4мин

Introduction to Hugging Face Spaces4мин

Hugging Face Spaces Walkthrough6мин

Deploying Hugging Face Spaces3мин

1 практическое упражнение

Applied Hugging Face30мин

Часто задаваемые вопросы

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