Informações sobre o curso

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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.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Nível avançado

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

Aprox. 13 horas para completar

Inglês

O que você vai aprender

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.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Nível avançado

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

Aprox. 13 horas para completar

Inglês

Instrutores

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

32.225 aprendizes

10 Cursos

Alfredo Deza

Senior Content Engineer

Microsoft

13.377 aprendizes

6 Cursos

Programa - O que você aprenderá com este curso

Semana

1

Semana 1

3 horas para concluir

Introduction to MLflow

3 horas para concluir

13 vídeos (Total 82 mín.), 2 leituras, 1 teste

13 videos

Meet your Course Instructor: Alfredo Deza3min

Overview of MLflow4min

Installing and Using MLflow5min

Introduction to the Tracking UI8min

Parameters, Version, Artifacts and Metrics10min

Working with MLflow Projects4min

Create an MLflow Project7min

Run Project from Remote Git Repositories3min

Connecting MLflow to Databricks5min

Components of an MLflow Package6min

Using a Registry with an MLflow Model5min

Referencing Artifacts with the API8min

Saving and Serving MLflow Models8min

2 leituras

Meet your Supporting Instructor: Noah Gift10min

Course Structure and Discussion Etiquette10min

1 exercício prático

MLflow30min

Semana

2

Semana 2

3 horas para concluir

Introduction to Hugging Face

3 horas para concluir

14 vídeos (Total 98 mín.)

14 videos

What is Hugging Face?5min

Overview of the Hugging Face Hub5min

Introduction to the Hugging Face Hub5min

Using Hugging Face Repositories7min

Using Hugging Face Spaces12min

Introduction to Applied Hugging Face1min

Using GPU Enabled Codespaces8min

Using the Hugging Face CLI2min

Using the Model Hub7min

Downloading Models7min

Working with Models9min

Adding Datasets6min

Using Datasets10min

Working with Datasets6min

1 exercício prático

Hugging Face Fundamentals30min

Semana

3

Semana 3

3 horas para concluir

Deploying Hugging Face

3 horas para concluir

13 vídeos (Total 76 mín.)

13 videos

Hugging Face and FastAPI4min

Containerizing Hugging Face3min

Running FastAPI with Hugging Face7min

CI/CD Packaging with GitHub Actions9min

Hugging Face and Azure ML Studio4min

Registering a Hugging Face Dataset on Azure7min

Registering a Hugging Face Model on Azure5min

Inspecting a Hugging Face Dataset on Azure2min

Azure ML Python SDK5min

Using GitHub Actions for Model Deployments5min

Using Azure Container Registry3min

Automating Packaging with Azure Container Registry7min

Automating Packaging with Docker Hub7min

1 exercício prático

Deploying Hugging Face30min

Semana

4

Semana 4

4 horas para concluir

Applied Hugging Face

4 horas para concluir

11 vídeos (Total 65 mín.)

11 videos

Create an Azure Container Application5min

Configure an Azure Container Application5min

Deploy Hugging Face to Azure12min

Troubleshooting Container Deployment4min

Introduction to Fine-Tuning Theory2min

Performing Fine-Tuning8min

Introduction to ONNX and Hugging Face8min

Exporting Hugging Face Models to ONNX4min

Introduction to Hugging Face Spaces4min

Hugging Face Spaces Walkthrough6min

Deploying Hugging Face Spaces3min

1 exercício prático

Applied Hugging Face30min

Perguntas Frequentes – FAQ

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