MLOps Tools: MLflow and Hugging Face

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01 - Introduction

01 - MLOps platforms introduction.mp4

02:55

02 - 1. Introduction to MLflow

01 - Overview of MLflow.mp4

04:12

02 - Installing and using MLflow.mp4

05:52

03 - Introduction to the tracking UI.mp4

08:32

04 - Parameters, version, artifacts, and metrics.mp4

10:11

03 - 2. MLflow Projects

01 - Working with MLflow projects.mp4

04:48

02 - Create an MLflow project.mp4

07:47

03 - Run projects from remote Git repositories.mp4

03:37

04 - Connecting MLflow to Databricks.mp4

05:18

04 - 3. MLflow Models

01 - Components of the MLflow package.mp4

05:57

02 - Use a registry with an MLflow model.mp4

05:05

03 - Referencing artifacts with the API.mp4

07:57

04 - Saving and serving MLflow models.mp4

07:59

05 - 4. Introduction to Hugging Face

01 - What is Hugging Face.mp4

05:32

02 - Overview of the Hugging Face Hub.mp4

04:57

03 - Introduction to the Hugging Face Hub.mp4

05:09

04 - Using Hugging Face repositories.mp4

07:44

05 - Using Hugging Face Spaces.mp4

12:32

06 - 5. Introduction to Applied Hugging Face

01 - Introduction to applied Hugging Face.mp4

01:36

02 - Using GPU-enabled Codespaces.mp4

08:14

03 - Using the Hugging Face CLI.mp4

02:19

07 - 6. Using Hugging Face

01 - Using the Model Hub.mp4

07:10

02 - Downloading models.mp4

07:42

03 - Working with models.mp4

09:34

04 - Adding datasets.mp4

06:38

05 - Using datasets.mp4

10:46

06 - Working with datasets.mp4

06:44

08 - 7. Packaging Hugging Face

01 - Hugging Face and FastAPI.mp4

04:09

02 - Containerizing Hugging Face.mp4

03:43

03 - Running FastAPI with Hugging Face.mp4

07:30

04 - CICD packaging with GitHub Actions.mp4

09:35

09 - 8. Hugging Face and Azure ML Studio

01 - Hugging Face and Azure ML Studio.mp4

04:35

02 - Registering a Hugging Face dataset on Azure.mp4

07:35

03 - Registering a Hugging Face model on Azure.mp4

05:38

04 - Inspecting a Hugging Face dataset on Azure.mp4

02:47

05 - Azure ML Python SDK.mp4

05:26

10 - 9. Hugging Face Automation

01 - Using GitHub Actions for model deployments.mp4

05:46

02 - Using Azure Container Registry.mp4

03:33

03 - Automating packaging with Azure Container Registry.mp4

07:05

04 - Automating packaging Docker Hub.mp4

05:53

11 - 10. Hugging Face with Azure Containers

01 - Create an Azure container application.mp4

05:03

02 - Configure an Azure container application.mp4

04:56

03 - Deploy Hugging Face to Azure.mp4

12:05

04 - Troubleshooting container deployment.mp4

04:01

12 - 11. Fine-Tuning and ONNX Exporting

01 - Introduction to fine-tuning theory.mp4

02:54

02 - Performing fine-tuning.mp4

08:09

03 - Introduction to ONNX and Hugging Face.mp4

08:50

04 - Exporting Hugging Face models to ONNX.mp4

03:58

13 - 12. Hugging Face Spaces

01 - Introduction to Hugging Face Spaces.mp4

04:46

02 - Hugging Face Spaces walkthrough.mp4

06:04

03 - Deploying to Hugging Face Spaces.mp4

03:14

Description

In this course, learn how to master MLflow and Hugging Face, two powerful open-source platforms for MLOps. Starting with MLflow, learn how to streamline the machine learning lifecycle, manage projects and models, use the tracking UI system, and interact with registered models. Then, get an introduction to Hugging Face, starting with an overview of the Hugging Face Hub, repositories, and Hugging Face Spaces. Learn how to collaborate and deploy models, store datasets and models, create live interactive demos, and leverage community repositories.

Note: This course was created by Pragmatic AI Labs. We are pleased to host this training in our library

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