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In today’s fast-paced and data-driven world, machine learning models are playing an increasingly significant role in making accurate predictions. To optimize efficiency, reliability, and responsiveness in processing realtime inference requests, it is essential to have a robust and high-performance machine learning infrastructure. Amazon SageMaker’s serverless inference enables realtime model inference requests without the need to provision compute instances or configure scaling policies, ensuring a seamless experience.
One challenge that often arises is the occurrence of “cold starts,” when serverless endpoints need to spin up compute resources to handle sudden spikes in requests. This challenge can be addressed using Amazon SageMaker provisioned concurrency, a highly effective solution for eliminating cold starts and optimizing performance.
Provisioned concurrency can be defined as pre-allocating a certain number of compute resources for frequently-invoked serverless inference endpoints. These resources, which are dedicated to the endpoint, can handle steady-state traffic more efficiently, thus mitigating the impact of cold starts.
Using provisioned concurrency with serverless inference can provide numerous benefits, including:
For many applications, the demand for inference varies over time, so enabling Application Auto Scaling in SageMaker helps to scale provisioned concurrency dynamically. This ensures that the right amount of resources are available at the right time, further optimizing the performance and cost-efficiency of the serverless infrastructure.
Some advantages of using Application Auto Scaling with provisioned concurrency include:
To maximize the benefits of Amazon SageMaker serverless inference and provisioned concurrency, consider the following best practices:
Amazon SageMaker’s serverless inference, coupled with provisioned concurrency, offers a powerful solution to optimize machine learning model performance. By eliminating cold starts and providing predictable performance characteristics, these techniques enable data scientists, engineers, and decision-makers to deliver faster, more reliable, and cost-effective machine learning applications. Don’t hesitate to adopt these innovative techniques on your journey to build powerful and efficient machine learning models.
Up until working with Casey, we had only had poor to mediocre experiences outsourcing work to agencies. Casey & the team at CJ&CO are the exception to the rule.
Communication was beyond great, his understanding of our vision was phenomenal, and instead of needing babysitting like the other agencies we worked with, he was not only completely dependable but also gave us sound suggestions on how to get better results, at the risk of us not needing him for the initial job we requested (absolute gem).
This has truly been the first time we worked with someone outside of our business that quickly grasped our vision, and that I could completely forget about and would still deliver above expectations.
I honestly can’t wait to work in many more projects together!
Disclaimer
*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.