Welcome to the next step in your SageMaker deployment journey! You've already mastered two important deployment patterns: first, you learned how to deploy locally trained models to SageMaker serverless endpoints, which taught you the fundamental concepts of model packaging, S3 uploads, and entry point scripts. Then, you discovered how to deploy models trained with SageMaker estimators, which streamlined the process by leveraging SageMaker's built-in artifact management through the SKLearn.attach() method.

Now you're ready to explore another modern and advanced approach to model training and deployment within the SageMaker ecosystem: working with SageMaker's ModelTrainer. The ModelTrainer represents SageMaker's latest evolution in training workflows, providing enhanced capabilities for model development and more sophisticated training job management. While the deployment concepts you've learned remain consistent, ModelTrainer jobs require a slightly different approach to retrieve and deploy your trained models.

By the end of this lesson, you'll understand how to locate ModelTrainer jobs in your SageMaker environment, retrieve model artifacts from completed training jobs, create custom entry point scripts for inference handling, configure and deploy models using the SKLearnModel class with serverless inference, and test your deployed endpoints to ensure they're performing as expected. This knowledge will complete your understanding of the three primary deployment patterns in SageMaker and prepare you to work with the most advanced training workflows available on the platform.

SageMaker's ModelTrainer represents an advanced training framework that provides enhanced capabilities for model development compared to traditional estimators. When you use ModelTrainer for training, it creates training jobs with specific naming patterns and organizational structures that differ from the estimator jobs you worked with in the previous lesson.

To work with ModelTrainer deployments, you first need to understand how to identify and retrieve these specialized training jobs. As you've done in previous lessons, you'll start by initializing a SageMaker session and querying for completed training jobs, but this time you'll filter specifically for ModelTrainer jobs:

Since you already used sklearn-modeltrainer as the base name for your training jobs in a previous course, you can use the NameContains='sklearn-modeltrainer' filter to easily identify your ModelTrainer jobs. This naming convention distinguishes your ModelTrainer jobs from regular estimator jobs, which typically use names like "sagemaker-scikit-learn."

Recall that, unlike estimator jobs where you could use the SKLearn.attach() method to recreate the estimator object, ModelTrainer jobs require a different approach. You cannot attach to ModelTrainer jobs directly because they use a different internal structure and artifact organization. Instead, you need to extract the model artifacts from the training job details and create a new model object for deployment. This process gives you more control over the deployment configuration but requires a few additional steps compared to the estimator attachment method.

First, you need to retrieve the training job details to locate where SageMaker stored your trained model artifacts:

The describe_training_job() method returns comprehensive information about your completed training job, including the S3 location where SageMaker automatically stored your model artifacts. The ModelArtifacts section contains the S3ModelArtifacts path, which points to the compressed model file that contains everything needed for inference.

Unlike estimator deployments where you could attach directly to training jobs, ModelTrainer jobs require you to create a separate entry point script for inference. Even if your original training script contained inference functions, ModelTrainer doesn't automatically package this code for deployment the way estimators do.

Because of that, let's use an entry_point.py file with the model loading logic:

The key difference here is that you must explicitly provide this script for ModelTrainer deployments, whereas estimator deployments handled this automatically through the attachment process.

The model creation follow the same patterns from previous lessons, with the key difference being that you use the model_data retrieved from the ModelTrainer job:

This follows the same deployment pattern as locally trained models, but leverages the artifacts that SageMaker automatically stored during your ModelTrainer job.

With your model object configured, deploy it using the same process from previous lessons:

The deployment process is identical to your previous deployments - you'll see the same "Creating" status initially, progressing to "InService" when ready for predictions.

Congratulations! You've now mastered the three primary deployment patterns in SageMaker: locally trained models, estimator models, and ModelTrainer models. This lesson equipped you with the skills to work with SageMaker's most advanced training framework, from identifying ModelTrainer jobs to creating custom entry point scripts and deploying with full configuration control.

Before we move forward, it's crucial that you solidify these ModelTrainer concepts through hands-on practice. The upcoming practice exercises will challenge you to apply everything you've learned about ModelTrainer deployments, ensuring you're comfortable with this advanced pattern before we explore new territory.

Once you've mastered ModelTrainer deployments through practice, we'll transition to real-time endpoints—a completely different deployment approach with persistent infrastructure, auto-scaling capabilities, and production-grade performance characteristics. Real-time endpoints open up exciting possibilities for high-throughput applications and always-on services that serverless endpoints can't provide.

Ready to put your ModelTrainer skills to the test? Let's dive into the practice exercises!