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- Guidance for Optimizing MLOps for Sustainability on AWS
Guidance for Optimizing MLOps for Sustainability on AWS
Overview
This Guidance demonstrates how to implement environmentally sustainable MLOps practices across the entire machine learning lifecycle. It helps organizations optimize their ML workflows for both performance and energy efficiency, addressing the growing environmental impact of increasingly complex ML models. The solution shows how to reduce carbon emissions at every phase, from data collection and storage to model training and inference, while maintaining operational excellence. Furthermore, it demonstrates how organizations can align their MLOps practices with net-zero goals through practical best practices, enabling them to achieve both their ML objectives and sustainability targets without compromising on model performance.
Benefits
Optimize your machine learning operations to achieve up to 52% lower energy consumption using purpose-built infrastructure. Track and minimize environmental impact while maintaining model performance.
Deploy automated MLOps pipelines that eliminate redundant training runs and optimize resource utilization. Reduce time-to-market while minimizing computational waste through intelligent experiment tracking.
Implement automated data lifecycle management that moves ML artifacts between storage tiers based on access patterns. Reduce unnecessary storage consumption while ensuring data availability.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
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