AI News, Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning

Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning

This feature allows developers and data scientists to save significant time and effort in training and tuning their machine learning models.

A Hyperparameter Tuning job launches multiple training jobs, with different hyperparameter combinations, based on the results of completed training jobs.

developer’s typical machine learning process comprises 4 steps: exploratory data analysis (EDA), model design, model training, and model evaluation.

SageMaker already makes each of those steps easy with access to powerful Jupyter notebook instances, built-in algorithms, and model training within the service.

Focusing on the training portion of the process, we typically work with data and feed it into a model where we evaluate the model’s prediction against our expected result.

We keep a portion of our overall input data, the evaluation data, away from the training data used to train the model.

In many cases after we’ve chosen an algorithm or built a custom model, we will need to search the space of possible hyperparameter configurations of that algorithm for the best results for our input data.

A user only needs to select the hyperparameters to tune, a range for each parameter to explore, and the total number of training jobs to budget.

There are few types of parameters: Now that we have our ranges defined we want to define our success metric and a regular expression for finding that metric in the training job logs.

Hopping back over to our notebook instance, we have a handy analytics object from tuner.analytics() that we can use to visualize the results of the training with some bokeh plots.

To take advantage of automatic model tuning there are really only a few things users have to define: the hyperparameter ranges, the success metric and a regex to find it, the number of jobs to run in parallel, and the maximum number of jobs to run.

Increasing max_parallel_jobs will cause the tuning job to finish much faster but a lower parallelism will generally provide a slightly better final result.

awslabs/amazon-sagemaker-examples

This repository contains example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.

These examples introduce SageMaker's hyperparameter tuning functionality which helps deliver the best possible predictions by running a large number of training jobs to determine which hyperparameter values are the most impactful.

They cover a broad range of topics and will utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker.

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