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AWS SageMaker Estimators and Models

Amazon SageMaker provides several built-in machine learning algorithms that you can use for a variety of problem types. SageMaker Python SDK includes Estimators for many of these algorithms, including K-means, Principal Components Analysis (PCA), Linear Learner, Factorization Machines, Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM), Random Cut Forest, k-nearest neighbors (k-NN), Object2Vec, and IP Insights.

For the full list of algorithms, visit the AWS documentation.

Definition and usage

Estimators that wrap Amazon's built-in algorithms define algorithm's hyperparameters with defaults. When a default is not possible you need to provide the value during construction, e.g.:

  • KMeans Estimator requires parameter k to define number of clusters
  • PCA Estimator requires parameter num_components to define number of principal components

Interaction is identical as any other Estimators. There are additional details about how data is specified.

Input data format

Please note that Amazon's built-in algorithms are working best with protobuf recordIO format. The data is expected to be available in S3 location and depending on algorithm it can handle dat in multiple data channels.

This package offers support to prepare data into required fomrat and upload data to S3. Provided class RecordSet captures necessary details like S3 location, number of records, data channel and is expected as input parameter when calling fit().

Function record_set is available on algorithms objects to make it simple to achieve the above. It takes 2D numpy array as input, uploads data to S3 and returns RecordSet objects. By default it uses train data channel and no labels but can be specified when called.

Please find an example code snippet for illustration:

from sagemaker import PCA
pca_estimator = PCA(role='SageMakerRole', train_instance_count=1, train_instance_type='ml.m4.xlarge', num_components=3)

import numpy as np
records = pca_estimator.record_set(np.arange(10).reshape(2,5))

pca_estimator.fit(records)

Predictions support

Calling inference on deployed Amazon's built-in algorithms requires specific input format. By default, this library creates a predictor that allows to use just numpy data. Data is converted so that application/x-recordio-protobuf input format is used. Received response is deserialized from the protobuf and provided as result from the predict call.