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edX

MLOps Tools: MLflow and Hugging Face

Pragmatic AI Labs via edX

Overview

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.

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)

\- 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)

Taught by

Alfredo Deza

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