Serve Scikit-Learn Models for Deployment with BentoML

Offered By
Coursera Project Network
In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

DockerMLOpsMachine LearningBentoMLScikit-Learn

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.