# Well-Architected machine learning design principles
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 Well-Architected ML design principles are a set of considerations used as the basis for a well-architected ML workload. 

 Following the [Well-Architected Framework](https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.html) guidelines, use these general design principles to facilitate good design in the cloud for ML workloads: 
+  **Assign ownership:** Apply the right skills and the right number of resources along with accountability and empowerment to increase productivity. 
+  **Provide protection:** Apply security controls to systems and services hosting model data, algorithms, computation, and endpoints. This facilitates secure and uninterrupted operations. 
+  **Enable resiliency:** Verify fault tolerance and the recoverability of ML models through version control, traceability, and explainability. 
+  **Enable reusability:** Use independent modular components that can be shared and reused. This assists to enable reliability, improve productivity, and optimize cost. 
+  **Enable reproducibility:** Use version control across components, such as infrastructure, data, models, and code. Track changes back to a point-in-time release. This approach enables model governance and audit standards. 
+  **Optimize resources:** Perform trade-off analysis across available resources and configurations to achieve optimal outcome. 
+  **Reduce cost:** Identify the potentials for reducing cost through automation or optimization, analyzing processes, resources, and operations. 
+  **Enable automation:** Use technologies, such as pipelining, scripting, and continuous integration (CI), continuous delivery (CD), and continuous training (CT), to increase agility, improve performance, sustain resiliency, and reduce cost. 
+  **Enable continuous improvement:** Evolve and improve the workload through continuous monitoring, analysis, and learning. 
+  **Minimize environmental impact:** Establish sustainability goals and understand the impact of ML models. Use managed services and adopt efficient hardware and software and maximize their utilization.