Amazon SageMaker - Monitoring & Optimizing

Monitoring Model Performance in Amazon SageMaker

Monitoring machine learning models is important step to ensure the model will perform as expected when deployed in production. Amazon SageMaker provides various tools to monitor models, track metrics, and detect performance degradation over time.

Amazon SageMaker Model Monitor

The Amazon SageMaker Model Monitor tool continuously tracks the quality of models in real-time. It monitors incoming data for any kind of inconsistencies and alerts you when the prediction by model varies from the expected one. This tool ensures that your models stay accurate and reliable always.

CloudWatch Integration

Another monitoring tool is CloudWatch. Amazon SageMaker easily integrates with Amazon CloudWatch to collect, track, and visualize performance metrics in real-time. It allows you to configure custom metrics, such as accuracy or latency.

Automated Retraining

Amazon SageMaker also supports automated retraining which allows you to set triggers to retrain models when certain conditions are met. By automating retraining, you ensure that your models stay up to date with the latest data.

Hyperparameter Tuning and Optimization

Hyperparameter tuning plays an important role in achieving the best performance from a ML model. Amazon SageMakers hyperparameter optimization feature allows you to automatically search for the best combination of hyperparameters for your model.

Implementing Hyperparameter Tuning in Amazon SageMaker

Amazon SageMakers automatic hyperparameter tuning is also known as hyperparameter optimization (HPO). It helps you to identify the best hyperparameters by running multiple training jobs with different parameter combinations.

Example

Given below is a basic Python code example for hyperparameter tuning in Amazon SageMaker −

from Amazon SageMaker.tuner import HyperparameterTuner, ContinuousParameter

# Define the hyperparameters to tune
hyperparameter_ranges = {
    'eta': ContinuousParameter(0.01, 0.2),
    'max_depth': ContinuousParameter(3, 10)
}

# Set up the tuner
tuner = HyperparameterTuner(
    estimator=xgboost_estimator,
    objective_metric_name='validation:accuracy',
    hyperparameter_ranges=hyperparameter_ranges,
    max_jobs=10,
    max_parallel_jobs=2
)

# Start tuning job
tuner.fit({"train": train_input, "validation": validation_input})