Enroll Course: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke
In the rapidly evolving world of machine learning, operational tools are essential for deploying and maintaining models effectively. The ‘MLOps Tools: MLflow and Hugging Face’ course on Coursera provides a hands-on approach to mastering these two powerful platforms. It’s an ideal resource for data scientists, ML engineers, and DevOps professionals looking to streamline their ML workflows.
The course begins with an introduction to MLflow, highlighting its robust tracking system, model registry, and project management capabilities. You’ll learn how to register runs, manage models, and ensure reproducibility across projects. The hands-on exercises make it easy to understand the full lifecycle management of models using MLflow.
Moving forward, the course explores Hugging Face, renowned for its extensive model repositories and datasets. You’ll learn how to utilize these resources through APIs and the web interface, making it straightforward to store, share, and deploy models. The practical deployment segment is particularly valuable, demonstrating how to containerize models with Docker and serve them via FastAPI. Using platforms like Azure and Docker Hub, you’ll understand how to automate deployments for scalable, production-ready solutions.
The final modules focus on fine-tuning pre-existing models, deploying them to Hugging Face Spaces, and troubleshooting deployment issues. This comprehensive approach ensures you gain practical skills applicable across various ML deployment scenarios.
Overall, this course is highly recommended for anyone interested in elevating their MLOps skills. It combines theoretical foundations with real-world applications, providing a complete toolkit to manage ML models efficiently from development to deployment.
Enroll Course: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke