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