Built-in Algorithms

Amazon SageMaker provides several built-in machine learning algorithms that you can use for a variety of problem types.

Algorithm Task
BlazingText Word2vec and text classification
DeepAR Forecasting forecasting scalar time series
Factorization Machines discrete recommendations
Image Classification to classify images
IP Insights learning the usage patterns for IPv4 addresses
K-Means clustering
K-Nearest Neighbors (k-NN) classification or regression
Latent Dirichlet Allocation (LDA) topic modeling
Linear Learner discrete classification or quantitative prediction
Neural Topic Model (NTM) topic modeling
Object2Vec neural embeddings of high-dimensional objects
Object Detection to detect and classify objects in images
Principal Component Analysis (PCA) dimensionality reduction
Random Cut Forest (RCF) detecting anomalous data points
Semantic Segmentation classification of every pixel in an image
Sequence-to-Sequence neural machine translation
XGBoost discrete classification or quantitative prediction

Amazon SageMaker provides containers for its built-in algorithms. However, containers are used behind the scenes when you use one of the Amazon SageMaker built-in algorithms, so you do not deal with them directly. You can train and deploy these algorithms from the Amazon SageMaker console, the AWS Command Line Interface (AWS CLI), a Python notebook, or the Amazon SageMaker Python SDK.

In this workshop, we will use some of these algorithms using Python notebooks.