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Alfredo Deza
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:
Read more
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:
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/8nncxo/mlops
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)
Read more
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)
Read more
\- Reading: Course Structure and Discussion Etiquette (10 minutes)
\- Reading: Getting Started and Best Practices (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Video: Overview of MLflow (4 minutes)
\- Video: Installing and Using MLflow (5 minutes)
\- Video: Introduction to the Tracking UI (8 minutes)
\- Video: Parameters, Version, Artifacts and Metrics (10 minutes)
\- Reading: What is MLFlow? (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Quiz: MLflow (30 minutes)
\- Video: Working with MLflow Projects (4 minutes)
\- Video: Create an MLflow Project (7 minutes)
\- Video: Run Project from Remote Git Repositories (3 minutes)
\- Reading: MLflow Projects (10 minutes)
\- Quiz: Introduction to MLFlow (30 minutes)
\- Ungraded Lab: MLflow Projects (60 minutes)
\- Video: Connecting MLflow to Databricks (5 minutes)
\- Video: Components of an MLflow Package (6 minutes)
\- Video: Using a Registry with an MLflow Model (5 minutes)
\- Video: Referencing Artifacts with the API (8 minutes)
\- Video: Saving and Serving MLflow Models (8 minutes)
\- Reading: MLflow Models (10 minutes)
\- Quiz: MLflow Projects (30 minutes)
\- Discussion Prompt: Meet and Greet (optional) (10 minutes)
\- Discussion Prompt: Let Us Know if Something's Not Working (10 minutes)
Module 2 - Introduction to Hugging Face
\- Video: What is Hugging Face? (5 minutes, preview)
\- Video: Overview of the Hugging Face Hub (5 minutes)
\- Video: Introduction to the Hugging Face Hub (5 minutes)
\- Video: Using Hugging Face Repositories (7 minutes)
\- Video: Using Hugging Face Spaces (12 minutes)
\- Reading: Hugging Face Hub (10 minutes)
\- Video: Introduction to Applied Hugging Face (1 minute)
\- Video: Using GPU Enabled Codespaces (8 minutes)
\- Video: Using the Hugging Face CLI (2 minutes)
\- Reading: Hugging Face CLI (10 minutes)
\- Video: Using the Model Hub (7 minutes)
\- Video: Downloading Models (7 minutes)
\- Video: Working with Models (9 minutes)
\- Video: Adding Datasets (6 minutes)
\- Video: Using Datasets (10 minutes)
\- Video: Working with Datasets (6 minutes)
\- Reading: Datasets (10 minutes)
\- Quiz: Hugging Face Fundamentals (30 minutes)
\- Ungraded Lab: Introduction to Hugging Face (60 minutes)
Module 3 - Deploying Hugging Face
\- Video: Hugging Face and FastAPI (4 minutes, preview)
\- Video: Containerizing Hugging Face (3 minutes)
\- Video: Running FastAPI with Hugging Face (7 minutes)
\- Video: CI/CD Packaging with GitHub Actions (9 minutes)
\- Reading: FastAPI (10 minutes)
\- Quiz: Deploying Hugging Face (30 minutes)
\- Video: Hugging Face and Azure ML Studio (4 minutes)
\- Video: Registering a Hugging Face Dataset on Azure (7 minutes)
\- Video: Registering a Hugging Face Model on Azure (5 minutes)
\- Video: Inspecting a Hugging Face Dataset on Azure (2 minutes)
\- Video: Azure ML Python SDK (5 minutes)
\- Reading: Azure ML Python SDK (10 minutes)
\- Quiz: Quiz-Packaging Hugging Face (30 minutes)
\- Video: Using GitHub Actions for Model Deployments (5 minutes)
\- Video: Using Azure Container Registry (3 minutes)
\- Video: Automating Packaging with Azure Container Registry (7 minutes)
\- Video: Automating Packaging with Docker Hub (6 minutes)
\- Reading: Docker Overview (10 minutes)
\- Quiz: Hugging Face and Azure (30 minutes)
\- Ungraded Lab: Packaging Hugging Face (60 minutes)
Module 4 - Applied Hugging Face
\- Video: Create an Azure Container Application (5 minutes, preview)
\- Video: Configure an Azure Container Application (5 minutes)
\- Video: Deploy Hugging Face to Azure (12 minutes)
\- Video: Troubleshooting Container Deployment (4 minutes)
\- Quiz: Applied Hugging Face (30 minutes)
\- Ungraded Lab: Deploying Hugging Face (60 minutes)
\- Video: Introduction to Fine-Tuning Theory (2 minutes)
\- Video: Performing Fine-Tuning (8 minutes)
\- Quiz: Quiz-Hugging Face with Azure Containers (30 minutes)
\- Video: Introduction to ONNX and Hugging Face (8 minutes)
\- Video: Exporting Hugging Face Models to ONNX (4 minutes)
\- Ungraded Lab: Hugging Face and ONNX (60 minutes)
\- Quiz: Quiz: Fine-Tuning and ONNX Exporting (30 minutes)
\- Video: Introduction to Hugging Face Spaces (4 minutes)
\- Video: Hugging Face Spaces Walkthrough (6 minutes)
\- Video: Deploying Hugging Face Spaces (3 minutes)
\- Reading: Regulatory Entrepreneurship (10 minutes)
\- Reading: Ethical Sourcing of Datasets (10 minutes)
\- Reading: Glaze (10 minutes)
\- Video: Profit Sharing Concepts (5 minutes)
\- Video: Tragedy of the GenAI commons (4 minutes)
\- Video: Game Theory of GenAI (4 minutes)
\- Video: Perfect Competition (2 minutes)
\- Video: Negative Externalities (3 minutes)
\- Video: Regulatory Entrepreneurship (4 minutes)
\- Reading: Next Steps (10 minutes)
\- Ungraded Lab: Final Jupyter TensorFlow Sandbox (60 minutes)
\- Ungraded Lab: VSCode Final Sandbox (60 minutes)
\- Ungraded Lab: Linux Desktop Final Desktop (60 minutes)
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save MLOps Tools: MLflow and Hugging Face to your list so you can find it easily later:
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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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Gain deeper insights into MLFlow projects by collaborating with peers, discussing implementation strategies, and resolving any uncertainties.
Browse courses on
MlFlow
Show steps
-
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.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice creating and tracking MLFlow experiments
Show steps
Reinforce your understanding of MLFlow's core functionality by regularly practicing the creation and tracking of experiments.
Browse courses on
MlFlow
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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.
Review key concepts of MLOps and MLFlow
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Gain deeper insights into MLFlow projects by collaborating with peers, discussing implementation strategies, and resolving any uncertainties.
Browse courses on
MlFlow
Show steps
Show steps
Show steps
- 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.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice creating and tracking MLFlow experiments
Show steps
Reinforce your understanding of MLFlow's core functionality by regularly practicing the creation and tracking of experiments.
Browse courses on
MlFlow
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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:
For more career information including salaries, visit:
OpenCourser.com/course/8nncxo/mlops
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8nncxo/mlops
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Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructor
Alfredo Deza
Session
March 19, 2024
- March 20, 2025
Language
English
Transcript
English
Good to know
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
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