Driving Cost Efficiency into AI Deep Learning Pipelines with FinOps
This paper gives an overview of FinOps cost and usage efficiency considerations for deep learning AI.
FinOps is about ensuring maximal value creation through investments in technology, and empowering everyone in your organization to better understand their role in that objective.
Several FinOps Capabilities help practitioners and their organizations generate more value. Planning and estimating the costs of new technologies and new workloads is just the beginning. Practitioners must also inform stakeholders so they can efficiently architect for cloud, whether this relates to cloud migration or designing cloud native applications. When operations are underway, engaging engineers in workload optimization, and performing rate optimization become key activities. Practitioners must also engage with others from intersecting disciplines to drive value across the business.
Ultimately, all of these FinOps Capabilities help organizations gain insight into their unit economics, or the value created (at the unit level) after the costs to generate that value are deducted. By employing the Capabilities that are relevant for your organization, FinOps Practitioners can help shift the conversation from cost cutting to value creation.
