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 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.


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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