Practical Machine Learning with AWS: Process, Build, Deploy, and Productionize Your Models Using AWS

© Himanshu Singh 2021

H. SinghPractical Machine Learning with AWS https://doi.org/10.1007/978-1-4842-6222-1_8

8. Running a Custom Algorithm in SageMaker

Himanshu Singh¹  

(1)

ALLAHABAD, Uttar Pradesh, India

 

In this chapter, you will see how to run an algorithm of your own, instead of using SageMaker’s built-in algorithms. Although SageMaker provides built-in algorithms for almost any kind of problem statement, many times we want to run our own custom model utilizing the power of SageMaker. We can do so effectively if we have working knowledge of Docker and hands-on knowledge of Python. In this chapter, we will create a custom random forest model for our Big Mart dataset. We will deploy the container in ECR and then train the model using SageMaker. ...