This course covers two of the most popular open source platforms for MLOps: MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories so that you can store datasets, models, and create live interactive demos. Through a series of hands-on exercises, learners will gain practical experience working with these open source platforms. By the end of the course, you will be able to apply MLOps concepts like fine-tuning and deploying containerized models to the Cloud. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their programming skills.
Offert par
Open Source Platforms for MLOps
Duke UniversityÀ propos de ce cours
11 114 consultations récentes
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau avancé
Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.
Approx. 13 heures pour terminer
Anglais
Ce que vous allez apprendre
Create new MLflow projects to create and register models.
Use Hugging Face models and datasets to build your own APIs.
Package and deploy Hugging Face to the Cloud using automation.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau avancé
Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.
Approx. 13 heures pour terminer
Anglais
Offert par
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
Introduction to MLflow
3 heures pour terminer
13 vidéos (Total 82 min), 2 lectures, 1 quiz
3 heures pour terminer
Introduction to Hugging Face
3 heures pour terminer
14 vidéos (Total 98 min)
3 heures pour terminer
Deploying Hugging Face
3 heures pour terminer
13 vidéos (Total 76 min)
4 heures pour terminer
Applied Hugging Face
4 heures pour terminer
11 vidéos (Total 65 min)
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