MLOps Tools: MLflow and Hugging Face

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.

Module 1 - Introduction to MLflow

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

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

\- 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: Key Terms (10 minutes)

\- Reading: MLflow Projects (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: MLflow Models (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: Hugging Face Hub (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: Hugging Face CLI (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: Datasets (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: FastAPI (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: Azure ML Python SDK (10 minutes)

\- Reading: Lesson Reflection (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: Key Terms (10 minutes)

\- Reading: Docker Overview (10 minutes)

\- Reading: Lesson Reflection (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)

\- Reading: Key Terms (10 minutes)

\- Reading: Lesson Reflection (10 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)

\- Reading: Key Terms (10 minutes)

\- Reading: Lesson Reflection (10 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: Key Terms (10 minutes)

\- Reading: Regulatory Entrepreneurship (10 minutes)

\- Reading: Ethical Sourcing of Datasets (10 minutes)

\- Reading: Glaze (10 minutes)

\- Reading: Lesson Reflection (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)