AWS, Cloud Computing

3 Mins Read

Selective Execution in Amazon SageMaker Pipelines

Voiced by Amazon Polly


Managing end-to-end workflows efficiently is crucial in the ever-evolving landscape of machine learning. Amazon SageMaker Pipelines is a powerful service that allows users to automate and scale their machine-learning workflows. Among its various features, Selective Execution stands out as a game-changer. This blog will explore the significance of Selective Execution in Amazon SageMaker Pipelines, its benefits, and how it can be harnessed effectively.

Introduction to Selective Execution

Selective Execution is a feature in Amazon SageMaker Pipelines that enables users to skip unnecessary steps in a workflow.

This is particularly valuable when dealing with large datasets or extensive processing, as it helps save time and resources. Rather than executing the entire pipeline, Selective Execution allows users to run only the steps that are affected by changes in the data or code selectively.

Pioneers in Cloud Consulting & Migration Services

  • Reduced infrastructural costs
  • Accelerated application deployment
Get Started

Key Benefits

  1. Efficiency Improvement: Selective Execution significantly improves workflow efficiency. Users can choose to execute only the relevant steps instead of running the entire pipeline every time there’s a change. This is especially advantageous in scenarios where retraining models or processing large datasets is time-consuming.
  2. Cost Savings: By skipping unnecessary steps, users can reduce the consumption of computing resources, leading to cost savings. This is critical for organizations aiming to optimize their machine-learning workflows while managing their infrastructure costs effectively.
  3. Flexible Iterative Development: Execution facilitates a more agile and iterative development. Data scientists and engineers can make incremental changes to their models or data preprocessing steps and quickly test only the affected parts of the pipeline without waiting for the entire process to be completed.
  4. Enhanced Collaboration: When working on collaborative projects, Selective Execution ensures that team members can focus on their specific contributions without disrupting the entire workflow. This promotes smoother collaboration, as changes in one area can be isolated and tested independently.

How to Harness the Power of Selective Execution?

  1. Pipeline Definition: Define your machine learning pipeline using the Amazon SageMaker Pipelines SDK. Identify the dependencies between different steps to enable Selective Execution to understand the relationships within the pipeline.
  1. Configure Step Dependencies: Explicitly define dependencies between pipeline steps. This is crucial for Selective Execution to determine the impact of changes on subsequent steps.
  1. Enable Caching: Leverage caching to store the output of a step and reuse it when the step is not modified. This is a powerful optimization that further enhances the efficiency gains of Selective Execution.
  1. Use Conditional Execution: Implement conditional execution based on specific artifacts or code changes. This allows you to fine-tune which steps are executed depending on the modifications.


Amazon SageMaker Pipelines’ Selective Execution feature brings efficiency, cost savings, and flexibility to the machine learning workflow. Data scientists and engineers can iterate more rapidly, collaborate seamlessly, and optimize resource utilization by intelligently skipping unnecessary steps. Understanding how to harness the power of Selective Execution is key to unlocking the full potential of Amazon SageMaker Pipelines in modern machine learning development.

In conclusion, as machine learning workflows evolve, Selective Execution is a testament to the commitment to making these processes more efficient, cost-effective, and user-friendly. Embracing this feature empowers organizations to build robust and scalable machine learning pipelines that adapt to the dynamic nature of data science projects.

Drop a query if you have any questions regarding Amazon SageMaker Pipelines and we will get back to you quickly.

Making IT Networks Enterprise-ready – Cloud Management Services

  • Accelerated cloud migration
  • End-to-end view of the cloud environment
Get Started

About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

CloudThat is recognized as a top-tier partner with AWS and Microsoft, including the prestigious ‘Think Big’ partner award from AWS and the Microsoft Superstars FY 2023 award in Asia & India. Having trained 650k+ professionals in 500+ cloud certifications and completed 300+ consulting projects globally, CloudThat is an official AWS Advanced Consulting Partner, AWS Training Partner, AWS Migration Partner, AWS Data and Analytics Partner, AWS DevOps Competency Partner, Amazon QuickSight Service Delivery Partner, Amazon EKS Service Delivery Partner, Microsoft Gold Partner, AWS Microsoft Workload Partners, Amazon EC2 Service Delivery Partner, and many more.

To get started, go through our Consultancy page and Managed Services PackageCloudThat’s offerings.


1. Can Selective Execution be applied to any pipeline in Amazon SageMaker?

ANS: – Yes, Selective Execution can be applied to any pipeline created using the Amazon SageMaker Pipelines SDK. However, its effectiveness depends on how well dependencies between steps are defined.

2. How does Selective Execution handle changes in code?

ANS: – Selective Execution can detect code changes associated with a specific step and trigger the execution of that step and its downstream dependencies.

3. Can I use Selective Execution with my existing Amazon SageMaker Pipelines?

ANS: – Yes, you can incorporate Selective Execution into existing pipelines by defining dependencies and configuring conditional execution for relevant steps.


Huda is working as the Front-end Developer in Cloudthat Technologies. She is experienced in building and maintaining responsive websites. She is keen on learning about new and emerging technologies. In addition to her technical skills, she is a highly motivated and dedicated professional, committed to delivering high quality work.



    Click to Comment

Get The Most Out Of Us

Our support doesn't end here. We have monthly newsletters, study guides, practice questions, and more to assist you in upgrading your cloud career. Subscribe to get them all!