MLOps packaging : HuggingFace and Docker.

MLOps packaging: HuggingFace and Docker Hub Use automation to package models Learn how to package a HuggingFace GPT2 model using automation with MLOps and pushing the result to Docker Hub. With just a little bit of Python and FastAPI you can have a powerful text generation API that is self-documente...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Place of publication not identified] : Pragmatic AI Solutions, [2022]
Edición:[First edition].
Temas:
Machine learning.
Computer software > Development.
Apprentissage automatique.
Computer software > Development
Machine learning
Instructional films
Internet videos
Nonfiction films
Instructional films.
Nonfiction films.
Internet videos.
Films de formation.
Films autres que de fiction.
Vidéos sur Internet.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:MLOps packaging: HuggingFace and Docker Hub Use automation to package models Learn how to package a HuggingFace GPT2 model using automation with MLOps and pushing the result to Docker Hub. With just a little bit of Python and FastAPI you can have a powerful text generation API that is self-documented! Learn Objectives In this video lesson, I'll go over the details with an example repository you can use for reference including the following learning objectives: Create a FastAPI application with HuggingFace Interact with the model with HTTP from a container using FastAPI Containerize the application using GitHub Actions Create repository secrets to login and push to Docker Hub Resources Example repository Practical MLOps book MLOps Maturity Model Packaging ML models.
Descripción Física:1 online resource (1 video file (16 min.)) : sound, color.
Tiempo de Juego:00:16:00

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