MLOps Tools: MLflow and Hugging Face

Go to Course: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke

Introduction

### Course Review: MLOps Tools: MLflow and Hugging Face on Coursera In the fast-evolving world of machine learning and artificial intelligence, mastering the best tools for managing and deploying machine learning models is crucial. The Coursera course titled **MLOps Tools: MLflow and Hugging Face** offers a comprehensive and hands-on introduction to two of the most significant platforms in the field: MLflow and Hugging Face. #### Course Overview This course provides learners with the fundamental skills needed to work effectively with MLflow and Hugging Face, two essential open-source platforms for Machine Learning Operations (MLOps). As the demand for efficient model management increases, the knowledge gained from this course will prove invaluable for anyone looking to deepen their understanding of MLOps practices. Over the span of the course, participants will engage in hands-on projects and real-world scenarios that will bolster their confidence and competence in using these tools. #### Syllabus Breakdown 1. **Introduction to MLflow** - The course kicks off with a thorough exploration of MLflow, a platform designed for managing the ML lifecycle. - Learners will install MLflow and conduct fundamental operations, including registering runs, models, and artifacts. - Participants will create an MLflow project, which emphasizes the importance of reproducibility in machine learning. The introduction also covers the use of a model registry and how to reference artifacts from the API, laying a solid foundation for effective model management. 2. **Introduction to Hugging Face** - The second week focuses on Hugging Face, a powerhouse in the world of natural language processing (NLP) and transformer models. - Learners will dive into the platform's repositories, learning to store models and datasets efficiently. - The week culminates in familiarizing participants with the Hugging Face APIs and web interface, ensuring they can utilize the platform's extensive capabilities. 3. **Deploying Hugging Face** - Here, participants will learn to containerize Hugging Face models, facilitating easier deployment and scaling. - By using the FastAPI framework, learners will set up interactive HTTP API endpoints for their models. - The course emphasizes automation for speed and reproducibility, with practical exercises on deploying models using Azure and Docker Hub. 4. **Applied Hugging Face** - The final week is dedicated to fine-tuning pre-existing Hugging Face models with additional datasets, reinforcing the idea of adaptive learning. - Deployments will again make use of Azure, with a focus on troubleshooting common issues, ensuring that learners are well-equipped to handle real-world challenges. - Additionally, participants will discover how to deploy models to Hugging Face Spaces, expanding their deployment skills to the platform's user-friendly environment. #### Why You Should Enroll If you are a data scientist, machine learning engineer, or software developer looking to delve deeper into MLOps, this course is highly recommended. The structured approach allows for gradual learning, from basic operations to advanced deployment techniques. - **Hands-On Learning**: Each module is designed with practical applications, ensuring that learners can apply concepts directly to real-world scenarios. - **Strong Community Support**: Coursera's platform facilitates interaction with peers and instructors, fostering a collaborative learning environment. - **Industry-Relevant Skills**: As both MLflow and Hugging Face are widely used in the industry, mastering these tools will enhance your marketability and open up new career opportunities. - **Flexibility**: With the ability to learn at your own pace, you can balance this course alongside other commitments without feeling rushed. In conclusion, the **MLOps Tools: MLflow and Hugging Face** course on Coursera stands out as a practical and essential resource for anyone aiming to sharpen their machine learning operations skills. The combination of theory, hands-on projects, and exposure to industry-leading tools makes this course a worthwhile investment in your professional development. Don't miss this opportunity to become proficient in MLOps and take your machine learning expertise to the next level!

Syllabus

Introduction to MLflow

This week, you will learn what MLflow is and how to use it. You’ll install MLflow and perform basic operations like registering runs, models, and artifacts. Then, you’ll create an MLflow project for reproducible results. Finally, you’ll understand how to use a registry with MLflow models and reference artifacts from the API.

This week, you will learn the basics of the Hugging Face platform. You will use some of its features like its repositories so that you can store models and datasets. Finally, you will learn how to add and use models and datasets using Hugging Face APIs as well as the web interface.

This week, you will learn how to containerize Hugging Face models and use the FastAPI framework to serve the model with an interactive HTTP API endpoint. Once you understand how to put everything together, you’ll use automation for speed and reproducibility. Finally, you’ll use Azure and Docker Hub to store the containers so that they can be used later for deployments.

This week, you will learn how to fine-tune Hugging Face models by using pre-existing models and then modifying (fine-tuning) them with additional data. You’ll also use Azure to deploy the container and learn how to troubleshoot it. Finally, you’ll also see how to deploy a model to Hugging Face spaces.