Pipelines overview
An Amazon SageMaker pipeline is a series of interconnected steps in directed acyclic graph (DAG)
that are defined using the drag-and-drop UI or Pipelines
SDK
The example DAG includes the following steps:
AbaloneProcess
, an instance of the Processing step, runs a preprocessing script on the data used for training. For example, the script could fill in missing values, normalize numerical data, or split data into the train, validation, and test datasets.AbaloneTrain
, an instance of the Training step, configures hyperparameters and trains a model from the preprocessed input data.AbaloneEval
, another instance of the Processing step, evaluates the model for accuracy. This step shows an example of a data dependency—this step uses the test dataset output of theAbaloneProcess
.AbaloneMSECond
is an instance of a Condition step which, in this example, checks to make sure the mean-square-error result of model evaluation is below a certain limit. If the model does not meet the criteria, the pipeline run stops.The pipeline run proceeds with the following steps:
AbaloneRegisterModel
, where SageMaker calls a RegisterModel step to register the model as a versioned model package group into the Amazon SageMaker Model Registry.AbaloneCreateModel
, where SageMaker calls a CreateModel step to create the model in preparation for batch transform. InAbaloneTransform
, SageMaker calls a Transform step to generate model predictions on a dataset you specify.
The following topics describe fundamental Pipelines concepts. For a tutorial describing the implementation of these concepts, see Pipelines actions.
Topics
- Pipeline Structure and Execution
- IAM Access Management
- Set up cross-account support for Pipelines
- Pipeline parameters
- Pipelines steps
- Lift-and-shift Python code with the @step decorator
- Pass Data Between Steps
- Caching pipeline steps
- Retry Policy for Pipeline Steps
- Selective execution of pipeline steps
- Baseline calculation, drift detection and lifecycle with ClarifyCheck and QualityCheck steps in Amazon SageMaker Pipelines
- Schedule Pipeline Runs
- Amazon SageMaker Experiments Integration
- Run pipelines using local mode
- Troubleshooting Amazon SageMaker Pipelines