SageMaker-ML-End2End

Leveraging the power of AWS SageMaker SDK to streamline the entire machine learning lifecycle.

How to Productionalize your ML Model

An example of how to take your model that you have created in Jupyter Notebook or Google Colab into a Production environment.

You can use SageMaker's drag-and-drop Pipeline Designer to visually create a pipeline. However, this code shows how you can generate your pipeline definition using the SageMaker Python SDK.

This code will create a Learning Pipeline, a modular, reproducible, scalable sequence of operations from within AWS SageMaker to transform raw data into actionable insights.

This will show how to deploy your ML project into AWS SageMaker, using its pre-built modules to execute the pipeline.

This code assumes you know how to access your AWS environment from the Console, and have basic AWS SageMaker knowledge on how to start a SageMaker Domain and a Notebook instance.

You can preview the Pipeline in SageMaker Studio Pipelines

This is the DAG Image of the Pipeline that is created by the following Notebook SageMaker SDK

SageMaker SDK Example

The provided Python notebook demonstrates how to use the SageMaker SDK to create an ML pipeline. Here's an overview of the key components:

1. Data Preparation: The code downloads and prepares the Abalone dataset for training.

2. Pipeline Parameters: Defines parameters for the pipeline, such as instance count and model approval status.

3. Processing Step: Uses SKLearnProcessor for data preprocessing.

4. Training Step: Configures and runs an XGBoost training job.

5. Evaluation Step: Processes the trained model to evaluate its performance.

6. Conditional Step: Implements a condition to check if the model meets performance criteria.

7. Model Registration: Registers the model if it passes the performance threshold.

8. Batch Transform: Performs batch transformation on a test dataset.

The pipeline combines these steps into a cohesive workflow, demonstrating how to:

• Create and configure various SageMaker step types
• Handle data processing and model training
• Implement conditional logic in the pipeline
• Register and deploy models based on performance criteria

This example provides a comprehensive template for building end-to-end ML pipelines using the SageMaker SDK, showcasing best practices for productionizing ML models in AWS SageMaker.