MLOps Tools: MLflow and Hugging Face from edX

Master MLFlow and Hugging Face, two powerful open-source platforms for MLOps:

MLflow : Streamline machine learning lifecycle

Manage projects and models
Use powerful tracking system
Interact with registered models
End-to-end lifecycle examples

Hugging Face:

Collaborate and deploy models
Store datasets and models
Create live interactive demos
Leverage community repositories

Key Takeaways:

Understand MLOps fundamentals
Fine-tune and deploy containerized models
Apply MLOps concepts to real-world use cases

Ideal for aspiring MLOps professionals or experienced practitioners looking to enhance their skills. Break into the field or level up your proficiency in machine learning operations.

What's inside

Learning objectives

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.

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.

Syllabus

Module 1 - Introduction to MLflow

\- Video: Meet your Course Instructor: Alfredo Deza (3 minutes, preview)

\- Reading: Meet your Supporting Instructor: Noah Gift (10 minutes)

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Appeals to aspiring MLOps professionals or experienced practitioners looking to enhance or refresh their skills

Taught by Alfredo Deza, who has a strong reputation for his work in the field

Examines industry-standard tools and solutions like MLFlow and Hugging Face

Develops practical skills that are essential for real-world MLOps applications

Develops a foundation in MLOps funamentals before moving on to advanced topics

Teaches to package and deploy Hugging Face models to the Cloud using automation

Reviews summary

Practical mlops with mlflow & hugging face

According to learners, this course offers a highly practical and up-to-date guide to MLOps using MLflow and Hugging Face. Students particularly praise its hands-on approach, which includes useful labs and demonstrations for model deployment and CI/CD integration. While it provides comprehensive coverage of these essential tools and their application, including detailed steps for Azure deployment, some learners note that the pace can be fast and it's best suited for those with existing foundational knowledge in machine learning and cloud platforms. Overall, it's considered an excellent resource for aspiring and experienced MLOps professionals.

Includes valuable discussions on ethical and business considerations.

"The inclusion of topics like regulatory entrepreneurship and ethical sourcing provided a holistic view of MLOps."

"It was interesting to consider the business implications of GenAI and the wider landscape of MLOps."

"Appreciated the modules that looked beyond just the tools to the wider impact of AI/ML in the real world."

Instructors are knowledgeable and experienced in the field.

"The instructors clearly have deep expertise in MLOps and the tools covered throughout the course."

"Alfredo Deza and Noah Gift provided valuable insights and their practical experience shone through the lessons."

"I felt confident learning from professionals who are truly practitioners in the MLOps field."

Strong emphasis on deploying models and automating pipelines.

"I particularly appreciated the sections on CI/CD with GitHub Actions and FastAPI integration."

"The detailed steps for deploying to Azure were very clear and immediately actionable for my projects."

"Learning to containerize Hugging Face models and automate their release was a key takeaway for me."

Covers highly relevant tools in the MLOps ecosystem.

"Getting a deep dive into MLflow and Hugging Face was exactly what I needed for my MLOps career."

"The focus on these industry-standard tools makes the course incredibly valuable for current roles."

"The breadth of topics around model tracking, versioning, and deployment is excellent."

Emphasizes practical application and real-world deployment.

"I found the ungraded labs extremely helpful for understanding real-world MLOps workflows."

"The course shines in showing how to deploy models to Azure with MLflow and Hugging Face in a very practical way."

"It's a great course for applying MLOps concepts directly to industry standard tools and scenarios."

Assumes prior knowledge; may be fast-paced for some learners.

"This course is definitely for intermediate to advanced learners; beginners might struggle without prior ML experience."

"It assumes foundational knowledge in Python, machine learning, and basic cloud concepts, so be prepared."

"The pace can be quite fast in certain modules, requiring me to re-watch videos and do additional research."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in MLOps Tools: MLflow and Hugging Face with these activities:

Review key concepts of MLOps and MLFlow

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Strengthen your foundational understanding of MLOps and MLFlow by revisiting course materials, reinforcing core concepts and ensuring a solid knowledge base.

Browse courses on MLOps

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Go through the course syllabus and identify the key concepts of MLOps, such as model lifecycle management and continuous integration.
Review the MLFlow documentation and tutorials to refresh your understanding of its features and functionality.

Reach out to experts in the field of MLOps for guidance

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Enhance your learning journey by connecting with experienced professionals in the field, seeking advice, and gaining insights that will accelerate your professional growth.

Browse courses on MLOps

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Identify potential mentors through professional networking events, online platforms, or industry contacts.
Reach out to your chosen mentors, briefly introducing yourself and expressing your interest in their guidance.

Discussion with peers to clarify MLFlow project concepts

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Gain deeper insights into MLFlow projects by collaborating with peers, discussing implementation strategies, and resolving any uncertainties.

Browse courses on MlFlow

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Find peers in the course or online community who are knowledgeable about MLFlow projects.
Organize regular virtual or in-person meetings to discuss project concepts.
Share experiences, ask questions, and collaborate on project ideas.

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Practice creating and tracking MLFlow experiments

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Reinforce your understanding of MLFlow's core functionality by regularly practicing the creation and tracking of experiments.

Browse courses on MlFlow

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Set up an MLFlow tracking server.
Create a new MLFlow experiment.
Log metrics and parameters to the experiment.

Follow tutorial to reinforce deep learning with Hugging Models

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Explore external tutorials to deepen understanding of topics introduced in the course, helping you gain a stronger foundation in MLFlow and Hugging Face.

Browse courses on Hugging Face

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Identify a reputable tutorial for deep learning with Hugging Models.
Follow the tutorial step by step, completing all exercises and activities.
Take notes on key concepts and techniques and incorporate them into your MLFlow and Hugging Face knowledge base.

Deploy Hugging Face models to various platforms

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Solidify your understanding of cloud deployment with Hugging Face by practicing deployment to multiple platforms, gaining hands-on experience with real-world scenarios.

Browse courses on Hugging Face

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Select a Hugging Face model and prepare it for deployment.
Choose target platforms for deployment, such as AWS, Azure, or Google Cloud.
Deploy the model to the selected platforms, following best practices for security and efficiency.

Build a data visualization dashboard using Hugging Face Spaces

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Demonstrate your mastery of Hugging Face by creating a data visualization dashboard that showcases your knowledge of model performance and deployment.

Browse courses on Hugging Face

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Gather data from MLFlow experiments or external sources.
Choose appropriate data visualization techniques for the insights you want to convey.
Use Hugging Face Spaces to create an interactive dashboard that presents the data visualizations.

Participate in Kaggle competitions using Hugging Face

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Apply your skills to real-world challenges by participating in Kaggle competitions that utilize Hugging Face, showcasing your proficiency in solving complex ML problems.

Browse courses on Hugging Face

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Identify a Kaggle competition that aligns with your interests and skill level.
Build and train Hugging Face models to tackle the competition's challenges.
Fine-tune your models and submit your results, aiming to achieve a high ranking.

Career center

Learners who complete MLOps Tools: MLflow and Hugging Face will develop knowledge and skills that may be useful to these careers:

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MLOps Tools

MLflow and Hugging Face

What's inside

Learning objectives

Syllabus

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Reviews summary

Practical mlops with mlflow & hugging face

Activities

Career center

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