Across all industries, machine learning (ML) models are getting deeper, workflows are getting more complex, and workloads are operating at larger scales. Significant effort and resources are put into making these models more accurate since this investment directly results in better products and experiences. On the other hand, making these models run efficiently in production is a non-trivial undertaking that’s often overlooked, despite being key to achieving performance and budget goals. In this post we cover how Exafunction and AWS Inferentia work together to unlock easy and cost-efficient deployment for ML models in production.
Exafunction is a start-up focused on enabling companies to perform ML at scale as efficiently as possible. One of their products is ExaDeploy, an easy-to-use SaaS solution to serve ML workloads at scale. ExaDeploy efficiently orchestrates your ML workloads across mixed resources (CPU and hardware accelerators) to maximize resource utilization. It also takes care of auto

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