Serve Scikit-Learn Models for Deployment with BentoML

提供方
Coursera Project Network
在此免费的指导 项目中,您将:

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

在面试中展现此实践经验

Clock1.5 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

必备条件

You should be competent in Python programming, be familiar with basic machine learning concepts, and have built predictive models with scikit-learn.

您要培养的技能

DockerMLOpsMachine LearningBentoMLScikit-Learn

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

常见问题

常见问题

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