MLREL02-BP02 Use a data pipeline - Machine Learning Lens
Implementation guidanceResources

MLREL02-BP02 Use a data pipeline

Automate the processing, movement, and transformation of data between different compute and storage services. This automation enables data processing that is fault tolerant, repeatable, and highly available.

Desired outcome: You achieve streamlined and consistent data processing workflows that automatically handle data movement and transformations. You can process your machine learning data with increased reliability, repeatability, and availability, while reducing manual effort and potential errors. Your data processing becomes more efficient and scalable, enabling you to focus on deriving insights rather than managing data logistics.

Common anti-patterns:

  • Manually moving and transforming data between systems.

  • Creating one-off scripts for data processing tasks.

  • Inconsistent data transformation processes across teams.

  • Neglecting error handling and recovery mechanisms in data workflows.

  • Not versioning data processing code or configurations.

Benefits of establishing this best practice:

  • Reduces manual errors and inconsistencies in data processing.

  • Increases repeatability and reliability of data transformations.

  • Enables fault tolerance and automatic recovery mechanisms.

  • Improves scalability of data processing workflows.

  • Facilitates collaboration through standardized data processing patterns.

  • Enhances traceability and governance of data transformations.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Data is the foundation of a machine learning workload, and how you handle this data directly impacts the quality of your ML models. Data pipelines automate and standardize the process of collecting, cleaning, transforming, and delivering data to your ML workflows. Without proper data pipelines, your ML initiatives can suffer from inconsistent data quality, limited reproducibility, and operational inefficiencies.

Creating effective data pipelines requires careful planning around data sources, transformation logic, error handling, and monitoring capabilities. By implementing automated data pipelines, you verify that your data preparation follows a consistent, repeatable process that can scale with your ML workload demands. This approach leads to more reliable models and faster deployment cycles.

AWS provides a comprehensive set of tools specifically designed to build robust ML data pipelines, with Amazon SageMaker AI offering integrated capabilities for the entire ML lifecycle. These tools enable you to focus on deriving insights from your data rather than managing infrastructure.

Implementation steps

  1. Assess your data processing requirements. Begin by identifying your data sources, required transformations, and destination systems. Document the flow of data from source to consumption, noting data quality requirements, validation rules, or business logic that must be applied during processing.

  2. Implement data preparation and wrangling processes. Establish comprehensive data preparation workflows that transform raw data into ML-ready formats. Use a combination of AWS services and tools to:

  3. Build automated ML workflows with SageMaker AI Pipelines. After creating your data preparation workflow, export it to Amazon SageMaker AI Pipelines to automate your entire ML workflow. SageMaker AI Pipelines helps you:

    • Create end-to-end ML workflows that combine data preparation, model training, evaluation, and deployment

    • Automate pipeline execution on a schedule or trigger-based approach

    • Track lineage of ML artifacts for governance

    • Implement quality gates to verify that models meet performance criteria

    • Version control your pipelines for reproducibility

  4. Implement error handling and monitoring. Configure your pipelines to handle errors gracefully and provide visibility into pipeline performance:

    • Set up retry mechanisms for transient failures

    • Create notification systems for critical pipeline failures

    • Implement logging throughout your pipeline steps

    • Use Amazon CloudWatch to monitor pipeline metrics and set up alerts

  5. Version and store your data artifacts. Maintain traceability of your data processing:

    • Store processed datasets in Amazon S3 with appropriate versioning

    • Use Amazon SageMaker AI Feature Store to create reusable feature repositories

    • Document data transformations and their business logic

    • Implement data lineage tracking to understand data provenance

  6. Integrate with your existing ML workflow. Connect your data pipelines to other components of your ML environment:

    • Feed processed data directly into model training jobs

    • Integrate with model registries and deployment pipelines

    • Establish feedback loops from model performance back to data preparation

  7. Scale your data processing as needed. Configure your pipelines to handle growing data volumes:

    • Use distributed processing for large datasets with services like Amazon EMR

    • Implement incremental processing patterns for streaming data

    • Configure compute resources appropriately for each pipeline stage

Resources

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