Examining model artifacts

A model artifact contains one or several files that are produced by a training job that are required for model deployment. The number and the nature of these files depend on the algorithm that was trained. As we've seen many times, the model artifact is stored as a model.tar.gz file, at the S3 output location defined in the estimator.

Let's look at some different examples. You can use the artifacts from the jobs we previously trained for this.

Examining artifacts for built-in algorithms

Most built-in algorithms are implemented with Apache MXNet, and their artifacts reflect this. For more information on MXNet, please visit https://mxnet.apache.org/. Let's get started:

1. Let's start from the artifact for the Linear Learner model we trained in Chapter 4, Training Machine Learning Models:

   $ tar xvfz model.tar.gz x model_algo-1
   $ unzip model_algo-1 archive:  model_algo-1 extracting: additional-params.json extracting...