Guidance for Optimizing MLOps for Sustainability on AWS

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This Guidance provides best practices to help you optimize machine learning (ML) operations (MLOps) for environmental sustainability. While customers across industries are committed to reducing their carbon footprints, ML workloads are becoming increasingly complex and consuming more energy and resources. This Guidance helps you review and refine your workloads to maximize utilization and minimize waste and the total resources deployed and powered to support your workload at all aspects of the ML lifecycle, including data collection, data storage, feature engineering, training, inference, and deployment.

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Architecture Diagram

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Data Preparation
Model Training and Tuning
Model Deployment and Management

Data Preparation
This architecture diagram focuses on data preparation. For more details about other aspects of the ML lifecycle, open the other tabs.

Step 4
Reduce the volume of data to be stored and adopt sustainable storage options to limit the carbon impact of your workload. Use energy-efficient, archival-class storage for infrequently accessed data, such as your raw data. If you can easily re-create an infrequently accessed dataset, like training, validation and test data, use the Amazon Simple Storage Service (Amazon S3) One Zone-Infrequent Access class to minimize the total data stored.

Manage the lifecycle of all your data and automatically enforce deletion timelines to minimize the total storage requirements of your workload using Amazon S3 Lifecycle policies. Amazon S3 Intelligent-Tiering will automatically move your data to the most energy-efficient access tier when access patterns change. Define data retention periods that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 1
Choose Region based on both business requirements and sustainability goals. When regulations and legal aspects allow, choose Regions near Amazon renewable energy projects and Regions where the grid has low published carbon intensity to host your data and workloads. When selecting a Region, aim to minimize data movement across networks—store your data close to your producers and train your models close to your data.

Step 2
Adopt a serverless architecture for your pipeline so it only provisions resources when work needs to be done. Use Amazon SageMaker Pipeline to avoid maintaining compute infrastructure at all times. You can extend a template provided by Amazon SageMaker Projects, such as MLOps template for model building, training, deployment and Amazon SageMaker Model Monitor.

Step 4
Reduce the volume of data to be stored and adopt sustainable storage options to limit the carbon impact of your workload. Use energy-efficient, archival-class storage for infrequently accessed data, such as your raw data. If you can easily re-create an infrequently accessed dataset, like training, validation and test data, use the Amazon Simple Storage Service (Amazon S3) One Zone-Infrequent Access class to minimize the total data stored.

Manage the lifecycle of all your data and automatically enforce deletion timelines to minimize the total storage requirements of your workload using Amazon S3 Lifecycle policies. Amazon S3 Intelligent-Tiering will automatically move your data to the most energy-efficient access tier when access patterns change. Define data retention periods that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 1
Choose Region based on both business requirements and sustainability goals. When regulations and legal aspects allow, choose Regions near Amazon renewable energy projects and Regions where the grid has low published carbon intensity to host your data and workloads. When selecting a Region, aim to minimize data movement across networks—store your data close to your producers and train your models close to your data.

Step 2
Adopt a serverless architecture for your pipeline so it only provisions resources when work needs to be done. Use Amazon SageMaker Pipeline to avoid maintaining compute infrastructure at all times. You can extend a template provided by Amazon SageMaker Projects, such as MLOps template for model building, training, deployment and Amazon SageMaker Model Monitor.

Click to enlarge

Step 4
Reduce the volume of data to be stored and adopt sustainable storage options to limit the carbon impact of your workload. Use energy-efficient, archival-class storage for infrequently accessed data, such as your raw data. If you can easily re-create an infrequently accessed dataset, like training, validation and test data, use the Amazon Simple Storage Service (Amazon S3) One Zone-Infrequent Access class to minimize the total data stored.

Manage the lifecycle of all your data and automatically enforce deletion timelines to minimize the total storage requirements of your workload using Amazon S3 Lifecycle policies. Amazon S3 Intelligent-Tiering will automatically move your data to the most energy-efficient access tier when access patterns change. Define data retention periods that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 1
Choose Region based on both business requirements and sustainability goals. When regulations and legal aspects allow, use one of the AWS Regions where the electricity consumed is attributable to 100% renewable energy or Regions where the grid has a published carbon intensity that is lower than other locations (or Regions). When selecting a Region, aim to minimize data movement across networks—store your data close to your producers and train your models close to your data.

Step 2
Adopt a serverless architecture for your pipeline so it only provisions resources when work needs to be done. Use Amazon SageMaker Pipeline to avoid maintaining compute infrastructure at all times. You can extend a template provided by Amazon SageMaker Projects, such as MLOps template for model building, training, deployment and Amazon SageMaker Model Monitor.

Step 4
Reduce the volume of data to be stored and adopt sustainable storage options to limit the carbon impact of your workload. Use energy-efficient, archival-class storage for infrequently accessed data, such as your raw data. If you can easily re-create an infrequently accessed dataset, like training, validation and test data, use the Amazon Simple Storage Service (Amazon S3) One Zone-Infrequent Access class to minimize the total data stored.

Manage the lifecycle of all your data and automatically enforce deletion timelines to minimize the total storage requirements of your workload using Amazon S3 Lifecycle policies. Amazon S3 Intelligent-Tiering will automatically move your data to the most energy-efficient access tier when access patterns change. Define data retention periods that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 1
Choose Region based on both business requirements and sustainability goals. When regulations and legal aspects allow, use one of the AWS Regions where the electricity consumed is attributable to 100% renewable energy or Regions where the grid has a published carbon intensity that is lower than other locations (or Regions). When selecting a Region, aim to minimize data movement across networks—store your data close to your producers and train your models close to your data.

Step 2
Adopt a serverless architecture for your pipeline so it only provisions resources when work needs to be done. Use Amazon SageMaker Pipeline to avoid maintaining compute infrastructure at all times. You can extend a template provided by Amazon SageMaker Projects, such as MLOps template for model building, training, deployment and Amazon SageMaker Model Monitor.
Model Training and Tuning
This architecture diagram focuses on model training and tuning. For more details about other aspects of the ML lifecycle, open the other tabs.

Step 9
Define acceptable performance criteria: evaluate the accuracy of your models using Amazon SageMaker Processing Jobs and make trade-offs between your model’s accuracy and its carbon footprint. Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 10
Use AWS Trainium to train deep learning models at up to 52% less energy than comparable Amazon Elastic Compute Cloud (Amazon EC2) instances. Consider Managed Spot Training, which takes advantage of unused Amazon EC2 capacity, to improve your overall resource efficiency and reduce idle capacity of cloud resources.

Step 5
For distributed training of large deep learning models, use Amazon SageMaker Model Parallelism Library in your training code to maximize usage of graphics processing units (GPUs).

Step 6
Use Amazon SageMaker Training Compiler to compile your deep learning models from their high-level language representation to hardware-optimized instructions to reduce training time. This can speed up deep learning model training by up to 50%.

Step 8
Use Amazon SageMaker Debugger to detect under-utilization of system resources and identify training problems. SageMaker Debugger built-in rules can monitor your training jobs and automatically stop them upon bug detection.

Step 9
Define acceptable performance criteria: evaluate the accuracy of your models using Amazon SageMaker Processing Jobs and make trade-offs between your model’s accuracy and its carbon footprint. Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 10
Use AWS Trainium to train deep learning models at up to 52% less energy than comparable Amazon Elastic Compute Cloud (Amazon EC2) instances. Consider Managed Spot Training, which takes advantage of unused Amazon EC2 capacity, to improve your overall resource efficiency and reduce idle capacity of cloud resources.

Step 6
Use Amazon SageMaker Training Compiler to compile your deep learning models from their high-level language representation to hardware-optimized instructions to reduce training time. This can speed up deep learning model training by up to 50%.

Step 8
Use Amazon SageMaker Debugger to detect under-utilization of system resources and identify training problems. SageMaker Debugger built-in rules can monitor your training jobs and automatically stop them upon bug detection.

Step 9
Define acceptable performance criteria: evaluate the accuracy of your models using Amazon SageMaker Processing Jobs and make trade-offs between your model’s accuracy and its carbon footprint. Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.

Click to enlarge

Step 9
Define acceptable performance criteria: evaluate the accuracy of your models using Amazon SageMaker Processing Jobs and make trade-offs between your model’s accuracy and its carbon footprint. Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 10
Use AWS Trainium to train deep learning models at up to 52% less energy than comparable Amazon Elastic Compute Cloud (Amazon EC2) instances. Consider Managed Spot Training, which takes advantage of unused Amazon EC2 capacity, to improve your overall resource efficiency and reduce idle capacity of cloud resources.

Step 5
For distributed training of large deep learning models, use Amazon SageMaker Model Parallelism Library in your training code to maximize usage of graphics processing units (GPUs).

Step 6
Use Amazon SageMaker Training Compiler to compile your deep learning models from their high-level language representation to hardware-optimized instructions to reduce training time. This can speed up deep learning model training by up to 50%.

Step 9
Define acceptable performance criteria: evaluate the accuracy of your models using Amazon SageMaker Processing Jobs and make trade-offs between your model’s accuracy and its carbon footprint. Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.

Step 10
Use AWS Trainium to train deep learning models at up to 52% less energy than comparable Amazon Elastic Compute Cloud (Amazon EC2) instances. Consider Managed Spot Training, which takes advantage of unused Amazon EC2 capacity, to improve your overall resource efficiency and reduce idle capacity of cloud resources.

Step 6
Use Amazon SageMaker Training Compiler to compile your deep learning models from their high-level language representation to hardware-optimized instructions to reduce training time. This can speed up deep learning model training by up to 50%.

Step 9
Define acceptable performance criteria: evaluate the accuracy of your models using Amazon SageMaker Processing Jobs and make trade-offs between your model’s accuracy and its carbon footprint. Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.
Model Deployment and Management
This architecture diagram focuses on model deployment and management. For more details about other aspects of the ML lifecycle, open the other tabs.

Step 23
Monitor your ML model in production using SageMaker Model Monitor, automate model drift detection, and only retrain when predictive performance has fallen below defined key performance indicators (KPIs).

Step 14
Automate the deployment of your models. Use Amazon SageMaker Model Registry and AWS CodePipeline to run your deployment code.

Step 23
Monitor your ML model in production using SageMaker Model Monitor, automate model drift detection, and only retrain when predictive performance has fallen below defined key performance indicators (KPIs).

Click to enlarge

Step 14
Automate the deployment of your models. Use Amazon SageMaker Model Registry and AWS CodePipeline to run your deployment code.

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Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Operational Excellence

CloudWatch metrics and alarms monitor the health of model endpoints deployed on SageMaker hosting options, allowing you to record performance-related metrics, analyze metrics when events or incidents occur, establish KPIs to measure workload performance, and monitor and alarm proactively. Additionally, collecting and analyzing metrics for training jobs and inference environments using CloudWatch allows you to analyze workload health trends and conduct periodic workload metric reviews with your organization.

Read the Operational Excellence whitepaper
Security

AWS Identity and Access Management (IAM) controls access to resources and managed services to help ensure least privilege access, secure the ML environment, and protect against adversarial and malicious activities. Data is encrypted at rest on Amazon Simple Storage Service (Amazon S3) and SageMaker Feature Store, both of which use AWS Key Management Service (AWS KMS) to protect sensitive data.

Read the Security whitepaper
Reliability

SageMaker allows automatic scaling of the model endpoint for reliable processing of predictions and to meet changing workload demands. It also distributes instances across Availability Zones in case an outage occurs or an instance fails. SageMaker Pipelines allows for versioned pipeline inputs and artifacts, and SageMaker Projects allows for versioned data processing code. This versioning helps you create a repeatable approach and retain data in case you need to roll back to a previous state.

Read the Reliability whitepaper
Performance Efficiency

We selected the services in this Guidance to improve performance without compromising on accuracy of training results. For example, managed ML services, such as SageMaker, deliver better performance through pre-optimized ML components. SageMaker Inference Recommender increases performance while reducing inference time. High-compute instances, such as Trainium and Inferentia, can accelerate inference speed.

Read the Performance Efficiency whitepaper
Cost Optimization

SageMaker services have built-in features that help you optimize costs related to model training. For example, SageMaker Feature Store helps avoid the cost of storing and processing duplicated datasets. SageMaker Debugger allows you to stop a training job as soon as a bug is detected, saving costs associated with unnecessary training job executions. SageMaker Training Compiler reduces training time and costs on GPU instances. Serverless pipelines, SageMaker Asynchronous Endpoints, and SageMaker Batch Transform avoid the cost of maintaining compute infrastructure at all hours of the day.

Read the Cost Optimization whitepaper
Sustainability

SageMaker Serverless Inference Endpoints and SageMaker Asynchronous Endpoints use autoscaling groups to scale resources in response to demand. SageMaker Serverless Inference Endpoints scale endpoints down to zero when there are no requests. This minimizes unnecessary provisioned resources and reduces carbon emissions. Additionally, serverless technologies, such as SageMaker Serverless Inference Endpoints and SageMaker Pipeline help eliminate idle resources by not having to spin up servers.

Read the Sustainability whitepaper

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Architecture Diagram

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Optimizing MLOps for Sustainability

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Guidance for Optimizing MLOps for Sustainability on AWS

[SEO Subhead]

Architecture Diagram

Get Started

Try out this Guidance

Demo

Lab

Deploy this Guidance

Implementation guide

Sample code

Well-Architected Pillars

Related Content

Optimizing MLOps for Sustainability

Disclaimer

Was this page helpful?

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