01-digit-classification-linear-learner.md

Digit Classification with Linear Learner

In this lab we will introduce you to Amazon Sagemaker using the Amaazon-provided Linear Learner algorithm to perform binary classification of images of handwritten digits from the MNIST Database. Specifically, we'll train a model to identify whether or not a digit is a "0". In doing so, we will demonstrate how to use a Jupyter notebook and the SageMaker Python SDK to create a script to pre-process data, train a model, create a SageMaker hosted endpoint, and make predictions against this endpoint - completing a full machine learning workflow end-to-end.

1. In your notebook instance, click on the top level folder.
2. Navigate to sample-notebooks / introduction_to_amazon_algorithms / linear_learner_mnist
3. Open the linear_learner_mnist.ipynb notebook, the follow the directions in the notebook.
4. In the bucket = '<your_s3_bucket_name_here>' code line, paste the name of the S3 bucket you created in Module 1 to replace <your_s3_bucket_name_here>. The code line should now read similar to bucket = 'smworkshop-john-smith'. Do NOT paste the entire path (s3://.......), just the bucket name.

NOTE: training the model for this example typically takes about 5 minutes

Extra Credit

1. How good is the model? Compute precision, recall, and f1 metrics to find out.
2. Re-train the model to identify an other digit.
3. Try changing the classification algorithm (e.g. to a factorization machine) and repeating the workflow