Serve Scikit-Learn Models for Deployment with BentoML

ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

Build logistic regression models for text classification with scikit-learn

Create a Prediction Service with BentoML

Serve scikit-learn models with BentoML’s REST API model server

Containerize model servers with Docker for production deployments

Clock1.5 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

This is a hands-on project on serving your scikit-learn models for deployment with BentoML. By the time you complete this project, you will be able to build logistic regression models for text classification, serve scikit-learn models with BentoML's REST API model server, and containerize model servers with Docker for production deployments. Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with basic machine learning concepts, and have built predictive models with scikit-learn. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás

DockerMLOpsMachine LearningBentoMLScikit-Learn

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction and Project Overview

  2. Import Libraries and Load the Data

  3. scikit-learn Model Training and Evaluation

  4. Create a BentoService for Model Serving

  5. REST API Model Serving

  6. Send Prediction Requests to the REST API Server

  7. Containerize Model Server with Docker for Deployment

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Preguntas Frecuentes

Preguntas Frecuentes

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