Sagemaker Tutorial - 3 | Build Custom ML Model Container using Sagemaker

Hello, Guys, I am Spidy. I am back with another video.

This tutorial guides you through the process of building a custom algorithm in Amazon SageMaker using the SM-Docker template. You will learn how to create and deploy your own machine-learning algorithm.
Chapters:
00:00 - Video Introduction
00:25 - Introduction to the Video Topic
01:15 - Dividing Tasks for the Video
02:22 - Explaining the Folder Hierarchy in the Container
03:44 - Explanation of the Training Script
10:55 - Overview of Other Relevant Files
13:05 - Uploading Files to Sagemaker Studio
15:20 - Creating an IAM Role for Algorithm Building
17:28 - Executing Algorithm Building
18:11 - Reviewing Logs Using AWS CodeBuild
19:01 - Checking the Built Algorithm in ECR (Elastic Container Registry)
19:34 - Training the Model Using the Created Algorithm
24:33 - Creating a Model Endpoint
26:00 - Testing the Model Endpoint with Sample Data
29:33 - Resource Cleanup and Conclusion

Steps that we followed:
- Download SageMaker structure.
- Customize train & predictor.py.
- Configure Dockerfile dependencies.
- Organize files in SageMaker Studio.
- Create an algorithm container.
- Train your model.
- Set up a SageMaker Endpoint for testing.

Utilized AWS services:
- SageMaker Studio
- ECR (Elastic Container Registry)
- CodeBuild

Code ► https://github.com/Spidy20/Sagemaker-Tutorials
Sagemaker Official Structure ► https://github.com/aws/amazon-sagemaker-examples/tree/main/advanced_functionality/scikit_bring_your_own

AWS Tutorials ► https://youtube.com/playlist?list=PLsT53VV2LIIGnKRdYHMo-KO9uQcIhbFPs

Donate ► machinelearninghubai@okhdfcbank

Note: If you want me to solve your errors and make the project run into the system, I will do it using a remote desktop, and it will be paid. You can reach me at [email protected] for your queries.

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