MLCOST05-BP02 Explore cost effective hardware options
Machine learning models that power AI applications are becoming increasingly complex resulting in rising underlying compute infrastructure costs. Up to 90% of the infrastructure spend for developing and running ML applications is often on inference. Look for cost-effective infrastructure solutions for deploying their ML applications in production.
Desired outcome: You achieve significant cost savings while maintaining or improving the performance of your machine learning inference workloads. By implementing cost-effective hardware options, you optimize your infrastructure spend, reduce operational costs, and can allocate resources more efficiently across your ML applications. Your ML models run on purpose-built hardware that provides the right balance of performance and cost for your specific use case.
Common anti-patterns:
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Using general-purpose compute instances for ML workloads without considering specialized hardware options.
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Over-provisioning inference resources to handle peak loads without implementing scaling strategies.
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Ignoring model optimization opportunities before deploying to production.
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Selecting hardware based solely on performance metrics without considering cost-efficiency.
Benefits of establishing this best practice:
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Reduced infrastructure costs for ML model inference.
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Improved inference throughput and latency.
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More efficient use of computational resources.
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Lower total cost of ownership for AI applications.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Machine learning inference costs represent a significant portion of the total expenses associated with running ML workloads in production. As models become more complex, their computational requirements increase, which can lead to higher infrastructure costs. Selecting the right hardware for your ML workloads is crucial for maintaining cost efficiency without sacrificing performance.
AWS offers multiple options to optimize the cost and performance of your ML inference workloads. These include services that optimize models for specific hardware targets, instances that provide cost-effective acceleration for inference workloads, and deployment options that match your specific latency and throughput requirements.
Evaluating your specific workload requirements is essential before selecting hardware options. Consider factors such as latency requirements, throughput needs, model complexity, batch size capabilities, and budget constraints. This evaluation will assist you to determine the most appropriate hardware solution for your use case.
Implementation steps
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Use Amazon SageMaker AI Neo for model optimization. Amazon SageMaker AI Neo automatically optimizes machine learning models for inference on cloud instances and edge devices. For inference in the cloud, SageMaker AI Neo speeds up inference and saves cost by creating an inference optimized container in SageMaker AI hosting. For inference at the edge, SageMaker AI Neo saves developers months of manual tuning by automatically tuning the model for the selected operating system and processor hardware. Neo optimizes models trained in TensorFlow, PyTorch, MXNet, and other frameworks for deployment on ARM, Intel, and NVIDIA processors.
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Deploy on Amazon EC2 Inf2 Instances. Amazon EC2 Inf1 instances deliver high-performance ML inference at the lowest cost in the cloud. They deliver up to 2.3-times higher throughput and up to 70% lower cost per inference than comparable current generation GPU-based Amazon EC2 instances. Inf1 instances are built from the ground up to support machine learning inference applications. They feature up to 16 AWS Inferentia chips, high-performance machine learning inference chips designed and built by AWS. Additionally, Inf1 instances include second generation Intel Xeon Scalable processors and up to 100 Gbps networking to deliver high throughput inference.
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Explore Amazon EC2 Inf2 Instances. The second generation of AWS Inferentia-based instances, EC2 Inf2 instances, offer even greater performance improvements over previous generations. These instances are powered by AWS Inferentia2 chips and provide up to 4x higher throughput and up to 10x lower latency than Inf1 instances. They're ideal for more complex generative AI models and large language models (LLMs) that require high performance and cost-effective inference solutions.
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Consider Amazon SageMaker AI serverless inference. SageMaker AI serverless inference is a purpose-built inference option that automatically provisions, scales, and shuts down compute capacity based on your workload needs. This pay-per-use model can reduce costs by avoiding the need to continuously run instances when there are no inference requests, making it ideal for workloads with intermittent traffic patterns.
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Evaluate batch and asynchronous inference options. For non-real-time inference requirements, consider using SageMaker AI batch transform for offline inference processing or SageMaker AI asynchronous inference for workloads that can tolerate higher latency. These options often allow for more efficient resource utilization and lower costs compared to real-time inference endpoints.
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Implement automated scaling policies. Configure auto-scaling for your SageMaker AI endpoints to dynamically adjust the number of instances based on workload demands. This way, you can pay for the resources you need while maintaining performance requirements during peak usage periods.
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Use enhanced SageMaker AI Inference Recommender. Use SageMaker AI Inference Recommender with enhanced algorithms and support for multi-model endpoints to get sophisticated cost optimization recommendations for your specific workloads.
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Regularly monitor and analyze inference costs. Use AWS Cost Explorer and Amazon CloudWatch metrics to track your inference costs and performance metrics. Regularly review this data to identify optimization opportunities and adjust your hardware strategy accordingly.
Resources
Related documents:
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