Training an image classification model with the SageMaker Python SDK
As mentioned in the Getting started with the SageMaker Python SDK section, we can use built-in algorithms or custom algorithms (using scripts and custom Docker container images) when performing training experiments in SageMaker.
Data scientists and ML practitioners can get started with training and deploying models in SageMaker quickly using one or more of the built-in algorithms prepared by the AWS team. There are a variety of built-in algorithms to choose from and each of these algorithms has been provided to help ML practitioners solve specific business and ML problems. Here are some of the built-in algorithms available, along with some of the use cases and problems these can solve:
- DeepAR Forecasting: Time-series forecasting
- Principal Component Analysis: Dimensionality reduction
- IP Insights: IP anomaly detection
- Latent Dirichlet Allocation (LDA): Topic modeling
- Sequence-to-Sequence...
