Best Practices for Deploying Scalable and Reliable Machine Learning Models on AWS?

Question

What are the best practices for deploying machine learning models in production to ensure scalability and reliability on AWS?

[Davidayo](https://www.davidayo.com/) is working on deploying a machine learning model at scale and want to ensure it is both reliable and scalable. What are the key considerations when using services like AWS SageMaker, EC2, and Lambda for such deployments? Additionally, how do you handle model versioning, monitoring, and updating models in a live environment?

I've been exploring various tools and frameworks, and I came across an AI platform called [Gemini AI](https://www.davidayo.com/artificial-intelligence/), which I find quite interesting. For those interested, you can check it out here. I'd love to hear how you approach these challenges and whether you've found specific tools or strategies particularly useful.

Michael Shapira · Answer

The solution you are searching for is called MLOps. There are plenty of resources on the web. Just Google MLOps with Sage maker. I suggest starting with official AWS documentation

Didier Durand · Answer

Hi,

We have at AWS a Well-Architected framework described the best practices in multiple areas: there is one specific to AI / ML with SageMaker.

That is probably the place where you should start: https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/well-architected-machine-learning.html

This blog post may also be of interest for you: https://aws.amazon.com/blogs/machine-learning/fmops-llmops-operationalize-generative-ai-and-differences-with-mlops/

Best,

Didier

Best Practices for Deploying Scalable and Reliable Machine Learning Models on AWS?

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