MLCOST-02: Use managed services to reduce total cost of ownership (TCO) - Machine Learning Lens
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MLCOST-02: Use managed services to reduce total cost of ownership (TCO)

Evaluate adopting managed services and pay-per-usage. Using managed services enables organizations to operate more efficiently with reduced resources and reduced cost.

Implementation plan

  • Use Amazon managed ML services - Use Amazon SageMaker as a fully managed machine learning service to build, train, and deploy models at scale and at significantly lower costs. The total cost of ownership (TCO) of SageMaker over a three-year period is much lower than other self-managed cloud-based ML options, such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Kubernetes Service (Amazon EKS). SageMaker includes technologies such as Autopilot, Feature Store, Clarify, DataWrangler, Debugger, Studio, Training, Model deployment, Monitoring, and Pipelines.

  • Use Amazon managed AI services - AWS pre-trained AI services provide ready-made intelligence for your applications and workflows. AI services integrate with your applications to address common use cases, such as personalized recommendations, modernizing your contact center, improving safety and security, and increasing customer engagement. AI services on AWS don't require machine learning experience. They are fully managed, complete solutions with pay-as-you-go pricing and no upfront commitment.

  • Perform pricing model analysis - Analyze each component of the workload. Determine if the component and resources will be running for extended periods and eligible for commitment discounts, such as AWS Savings Plans. You can use Savings Plans to save on AWS usage in exchange for a commitment to a consistent amount of usage. Amazon SageMaker Savings Plans offer flexible attributes such as instance family, instance size, AWS Region, and component for your SageMaker instance usage.

Documents

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Videos