88 points by mlmaster01 7 months ago flag hide 11 comments
fraud_buster 7 months ago next
Excited about this real-world ML implementation! Going serverless can make scaling much less painful. Anyone tried this in production yet?
the_real_mlguy 7 months ago next
We've implemented a similar pipeline and can definitely agree on the benefits. However, simplified integration with on-prem systems was our major challenge.
serverlessdan 7 months ago next
The best approach for serverless depends on your existing infrastructure and compliances. Have you tried using a multi-cloud strategy?
the_real_mlguy 7 months ago next
We've found multi-cloud to be an overkill for our use case. Sticking with one provider greatly simplified our orchestration. Curious to hear about your experience, @serverlessDan
devopsjohn 7 months ago prev next
Did you consider using a managed service like AWS Lambda, GCP Cloud Functions or Azure Functions? Would love to hear your thoughts on potential limitations.
awsometech 7 months ago next
The choice of a provider usually depends on the existing tech stack, in-house expertise and budget. Fully managed services can lower the time-to-market significantly.
randomdev1 7 months ago prev next
Serverless ML pipelines? Isn't that just making things complicated for no reason? I'd rather keep my infrastructure lightweight without all this buzzword-worship.
awsometech 7 months ago next
Serverless and ML aren't merely buzzwords. They offer tangible benefits when it comes to reducing infrastructure overhead and scaling rapidly. Latencies may be more challenging, though.
mlsecuritypro 7 months ago prev next
Security and compliance are often overlooked when implementing ML pipelines. Auditing and policy enforcement need to be part of the solution.
fraud_buster 7 months ago next
True, that's something we spent a lot of time on. Using tools like CloudWatch and Config for AWS can greatly help with that.
randomdev1 7 months ago prev next
While I can see the benefits, I'm still unconvinced that the overhead for this is justifiable for small-scale use cases.